In [2]:
import cv2
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
%matplotlib inline
from moviepy.editor import VideoFileClip
In [3]:
# Used the Calibration image from the repository

img = mpimg.imread('camera_cal/calibration2.jpg')
org_img = img
plt.imshow(img)
Out[3]:
<matplotlib.image.AxesImage at 0x117d8df28>
In [4]:
# Initialized the objpts and imgpts for the chess board example

objpts = []
imgpts = []
objp = np.zeros((6*9, 3), np.float32)
objp[:,:2] = np.mgrid[0:9, 0:6].T.reshape(-1, 2)
In [5]:
# Conversion of BGR to Gray scale image, and used CV2 function to identify corners

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret, corners = cv2.findChessboardCorners(gray, (9,6),None)
print (ret)
True
In [6]:
# Used the CV2 drawChessboardCorners to plot the corners

if ret == True:
    imgpts.append(corners)
    objpts.append(objp)
    
    img - cv2.drawChessboardCorners(img, (8,6), corners, ret)
    plt.imshow(img)
In [7]:
# Used the CV2 calibrateCamera to calibrate using the Chessboard image with the corners

ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpts, imgpts, gray.shape[::-1], None, None)
In [8]:
# Function to get the undistort image with the give Chess board corner image

undist = cv2.undistort(img, mtx, dist, None, mtx)
plt.imshow(undist)
Out[8]:
<matplotlib.image.AxesImage at 0x11a8886d8>
In [9]:
# Just a show, to display both the original and undistorted image with corners plotted

org_img = mpimg.imread('camera_cal/calibration2.jpg')
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(org_img)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(undist, cmap='gray')
ax2.set_title('Undistorted with Corners', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
In [10]:
# Function to get the Perspective image with the given distorted image

def corners_unwarp_chess(img, nx, ny, mtx, dist):
    # Pass in your image into this function
    # Write code to do the following steps
    # 1) Undistort using mtx and dist
    # 2) Convert to grayscale
    # 3) Find the chessboard corners
    # 4) If corners found: 
            # a) draw corners
            # b) define 4 source points src = np.float32([[,],[,],[,],[,]])
                 #Note: you could pick any four of the detected corners 
                 # as long as those four corners define a rectangle
                 #One especially smart way to do this would be to use four well-chosen
                 # corners that were automatically detected during the undistortion steps
                 #We recommend using the automatic detection of corners in your code
            # c) define 4 destination points dst = np.float32([[,],[,],[,],[,]])
            # d) use cv2.getPerspectiveTransform() to get M, the transform matrix
            # e) use cv2.warpPerspective() to warp your image to a top-down view
    #delete the next two lines

    # Use the OpenCV undistort() function to remove distortion
    undist = cv2.undistort(img, mtx, dist, None, mtx)
    plt.imshow(undist)
    # Convert undistorted image to grayscale
    gray = cv2.cvtColor(undist, cv2.COLOR_BGR2GRAY)
    #plt.imshow(gray)
    # Search for corners in the grayscaled image
    ret, corners = cv2.findChessboardCorners(gray, (nx, ny), None)
    
    print (ret)

    if ret == True:
        # If we found corners, draw them! (just for fun)
        cv2.drawChessboardCorners(undist, (nx, ny), corners, ret)
        # Choose offset from image corners to plot detected corners
        # This should be chosen to present the result at the proper aspect ratio
        # My choice of 100 pixels is not exact, but close enough for our purpose here
        offset = 100 # offset for dst points
        # Grab the image shape
        img_size = (gray.shape[1], gray.shape[0])

        # For source points I'm grabbing the outer four detected corners
        src = np.float32([corners[0], corners[nx-1], corners[-1], corners[-nx]])
        # For destination points, I'm arbitrarily choosing some points to be
        # a nice fit for displaying our warped result 
        # again, not exact, but close enough for our purposes
        dst = np.float32([[offset, offset], [img_size[0]-offset, offset], 
                                     [img_size[0]-offset, img_size[1]-offset], 
                                     [offset, img_size[1]-offset]])
        # Given src and dst points, calculate the perspective transform matrix
        M = cv2.getPerspectiveTransform(src, dst)
        # Warp the image using OpenCV warpPerspective()
        warped = cv2.warpPerspective(undist, M, img_size)

        return warped, M

    return 
In [11]:
img = mpimg.imread('camera_cal/calibration2.jpg')
plt.imshow(img)
Out[11]:
<matplotlib.image.AxesImage at 0x11c11b198>
In [12]:
# Used the corners unwarp function to get the perpsective image

nx = 8 # the number of inside corners in x
ny = 6 # the number of inside corners in y
warped2, M = corners_unwarp_chess(img, nx, ny, mtx, dist)
True
In [13]:
# Just a plot to show both the original and undistorted Perspective image

org_img = mpimg.imread('camera_cal/calibration2.jpg')
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(org_img)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(warped2, cmap='gray')
ax2.set_title('Undistorted Perspective Image', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
In [74]:
# This function original image  to undistorted and perspective image

def corners_unwarp(img, mtx, dist):
    
    img_shape = img.shape
    
    print("Image shape:", img_shape)
    
    # Use the OpenCV undistort() function to remove distortion
    undist = cv2.undistort(img, mtx, dist, None, mtx)
    plt.imshow(undist)
    # Convert undistorted image to grayscale
    gray = cv2.cvtColor(undist, cv2.COLOR_BGR2GRAY)
    plt.imshow(gray)
    
    
    # Choose offset from image corners to plot detected corners
    # This should be chosen to present the result at the proper aspect ratio
    # My choice of 100 pixels is not exact, but close enough for our purpose here
    offset = 100 # offset for dst points
    # Grab the image shape
    img_size = (gray.shape[1], gray.shape[0])

    #corners = [[580, 460],[710,460],[1150,720],[150,720]]
    #corners = [[580, 460],[750,460],[1150,720],[150,720]]
    #corners = [[220,720], [1110, 720], [722, 470], [570, 470]]

    #corners_dst = [[320,720], [920, 720], [920, 1], [320, 1]]

    #src = np.array([[580, 460],[750,460],[1150,720],[150,720]]).astype(np.float32)
    #dst = np.array([[320,720], [920, 720], [920, 1], [320, 1]]).astype(np.float32)
    
    #Worksrc = np.array([[585, 460], [203, 720], [1127, 720], [695, 460]]).astype(np.float32)
    #Workdst = np.array([[320, 0], [320, 720], [960, 720], [960, 0]]).astype(np.float32)

    #work 2 src = np.array([[585, 460], [270, 720], [1127, 720], [695, 460]]).astype(np.float32)
    
    src = np.array([[585, 460], [280, 720], [1127, 720], [695, 460]]).astype(np.float32)
    dst = np.array([[320, 0], [320, 720], [960, 720], [960, 0]]).astype(np.float32)
    
    # For source points I'm grabbing the outer four detected corners
    #src = np.float32(corners)
    # For destination points, I'm arbitrarily choosing some points to be
    # a nice fit for displaying our warped result 
    # again, not exact, but close enough for our purposes
    #dst = np.float32([[offset, offset], [img_size[0]-offset, offset], 
                                     #[img_size[0]-offset, img_size[1]-0], 
                                     #[offset, img_size[1]-0]])
    
    #src = np.float32(corners)
    #dst = np.float32(corners_dst)
    # Given src and dst points, calculate the perspective transform matrix
    M = cv2.getPerspectiveTransform(src, dst)
    MInv = cv2.getPerspectiveTransform(dst, src)
    # Warp the image using OpenCV warpPerspective()
    warped = cv2.warpPerspective(undist, M, img_size)

    return warped, M, MInv, undist
In [277]:
img = mpimg.imread('test_images/test1.jpg')
plt.imshow(img)
Out[277]:
<matplotlib.image.AxesImage at 0x133b9df60>
In [76]:
# Called the corners_unwarp to get the Perspective image

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpts, imgpts, gray.shape[::-1], None, None)

warped2, M, Minv, undist = corners_unwarp(img, mtx, dist)
plt.imshow(warped2)
Image shape: (720, 1280, 3)
Out[76]:
<matplotlib.image.AxesImage at 0x1a4e113c8>
In [77]:
# Just a plot to show the Original Image and Undistorted Image

f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(img)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(undist, cmap='gray')
ax2.set_title('Undistorted Image', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
In [78]:
def abs_sobel_thresh(img, orient='x', sobel_kernel=3, thresh=(0, 255)):
    
    # Apply the following steps to img
    # 1) Convert to grayscale
    # 2) Take the derivative in x or y given orient = 'x' or 'y'
    # 3) Take the absolute value of the derivative or gradient
    # 4) Scale to 8-bit (0 - 255) then convert to type = np.uint8
    # 5) Create a mask of 1's where the scaled gradient magnitude 
            # is > thresh_min and < thresh_max
    # 6) Return this mask as your binary_output image
    
    
    gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
    # Apply x or y gradient with the OpenCV Sobel() function
    # and take the absolute value
    if orient == 'x':
        abs_sobel = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel))
    if orient == 'y':
        abs_sobel = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel))
    # Rescale back to 8 bit integer
    scaled_sobel = np.uint8(255*abs_sobel/np.max(abs_sobel))
    # Create a copy and apply the threshold
    binary_output = np.zeros_like(scaled_sobel)
    # Here I'm using inclusive (>=, <=) thresholds, but exclusive is ok too
    binary_output[(scaled_sobel >= thresh[0]) & (scaled_sobel <= thresh[1])] = 1

    return binary_output
In [79]:
def mag_thresh(img, sobel_kernel=3, mag_thresh=(0, 255)):
    
    # Apply the following steps to img
    # 1) Convert to grayscale
    # 2) Take the gradient in x and y separately
    # 3) Calculate the magnitude 
    # 4) Scale to 8-bit (0 - 255) and convert to type = np.uint8
    # 5) Create a binary mask where mag thresholds are met
    # 6) Return this mask as your binary_output image
    
     # Convert to grayscale
    gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
    # Take both Sobel x and y gradients
    sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
    sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
    # Calculate the gradient magnitude
    gradmag = np.sqrt(sobelx**2 + sobely**2)
    # Rescale to 8 bit
    scale_factor = np.max(gradmag)/255 
    gradmag = (gradmag/scale_factor).astype(np.uint8) 
    # Create a binary image of ones where threshold is met, zeros otherwise
    binary_output = np.zeros_like(gradmag)
    binary_output[(gradmag >= mag_thresh[0]) & (gradmag <= mag_thresh[1])] = 1


    return binary_output
In [80]:
# Run the function
mag_binary = mag_thresh(img, sobel_kernel=3, mag_thresh=(40, 200))
# Plot the result
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(img)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(mag_binary, cmap='gray')
ax2.set_title('Thresholded Magnitude', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
In [81]:
# Define a function that applies Sobel x and y, 
# then computes the direction of the gradient
# and applies a threshold.

def dir_threshold(img, sobel_kernel=3, thresh=(0, np.pi/2)):
    
    # Apply the following steps to img
    # 1) Convert to grayscale
    # 2) Take the gradient in x and y separately
    # 3) Take the absolute value of the x and y gradients
    # 4) Use np.arctan2(abs_sobely, abs_sobelx) to calculate the direction of the gradient 
    # 5) Create a binary mask where direction thresholds are met
    # 6) Return this mask as your binary_output image
    gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
    # Calculate the x and y gradients
    sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)
    sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)
    # Take the absolute value of the gradient direction, 
    # apply a threshold, and create a binary image result
    absgraddir = np.arctan2(np.absolute(sobely), np.absolute(sobelx))
    binary_output =  np.zeros_like(absgraddir)
    binary_output[(absgraddir >= thresh[0]) & (absgraddir <= thresh[1])] = 1

    return binary_output
In [82]:
# Run the function to get direction threshold

dir_binary = dir_threshold(img, sobel_kernel=15, thresh=(0.7, 1.3))
# Plot the result
f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(img)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(dir_binary, cmap='gray')
ax2.set_title('Thresholded Grad. Dir.', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
In [83]:
# Choose a Sobel kernel size

ksize = 3 # Choose a larger odd number to smooth gradient measurements

# Apply each of the thresholding functions
gradx = abs_sobel_thresh(img, orient='x', sobel_kernel=ksize, thresh=(10, 200))
grady = abs_sobel_thresh(img, orient='y', sobel_kernel=ksize, thresh=(10, 200))
mag_binary = mag_thresh(img, sobel_kernel=ksize, mag_thresh=(40, 200))
dir_binary = dir_threshold(img, sobel_kernel=ksize, thresh=(0, np.pi/2))
In [84]:
# Gets the combined image

combined = np.zeros_like(dir_binary)
combined[((gradx == 1) & (grady == 1)) | ((mag_binary == 1) & (dir_binary == 1))] = 1
In [85]:
# Plot to show both the original image, and Combined gradiant image

f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(img)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(combined, cmap='gray')
ax2.set_title('Combined Grad. Image', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
In [86]:
# To show the HLS usage with S channel, and plotted both original and combined image with S channel

hls = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
s = hls[:,:,2]
s_binary = np.zeros_like(combined)
s_binary[(s > 170) & (s < 255)] = 1
color_binary = np.zeros_like(combined)
color_binary[(s_binary > 0) | (combined > 0)] = 1

f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(img)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(color_binary, cmap='gray')
ax2.set_title('S channel + Combined Image', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
In [87]:
# Tested the historgram

import numpy as np
histogram = np.sum(color_binary[color_binary.shape[0]//2:,:], axis=0)
plt.plot(histogram)
Out[87]:
[<matplotlib.lines.Line2D at 0x11ea3b710>]
In [88]:
warped2, M, Minv, undist = corners_unwarp(img, mtx, dist)
plt.imshow(warped2)
Image shape: (720, 1280, 3)
Out[88]:
<matplotlib.image.AxesImage at 0x19fb5e6a0>
In [89]:
import numpy as np
histogram = np.sum(warped2[warped2.shape[0]//2:,:], axis=0)
# plt.plot(histogram)
In [294]:
# Main pipline code that does the Undistort, threshold, HLS/S-channel

def pipeline3(img):

    ksize = 3 # Choose a larger odd number to smooth gradient measurements
    #img = cv2.resize(img, (720, 405))
    #img = cv2.GaussianBlur(img, (ksize, ksize), 0)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpts, imgpts, gray.shape[::-1], None, None)

    warped, M, Minv, undist = corners_unwarp(img, mtx, dist)
   
    hls = cv2.cvtColor(warped, cv2.COLOR_RGB2HLS)
    
    s = hls[:,:,2]
    h = hls[:,:,0]
    l = hls[:,:,1]
    
        
    #plt.imshow(img)
    ksize = 7
    # Apply each of the thresholding functions
    gradx = abs_sobel_thresh(warped, orient='x', sobel_kernel=ksize, thresh=(40, 255))
    grady = abs_sobel_thresh(warped, orient='y', sobel_kernel=ksize, thresh=(40, 255))
    mag_binary = mag_thresh(warped, sobel_kernel=ksize, mag_thresh=(40, 200))
    #plt.imshow(mag_binary)
    dir_binary = dir_threshold(warped, sobel_kernel=ksize, thresh=(0, np.pi/2))
    
    combined = np.zeros_like(dir_binary)
    combined[((gradx == 1) & (grady == 1)) | ((mag_binary == 1) & (dir_binary == 1))] = 1
    

    s_binary = np.zeros_like(combined)
    h_binary = np.zeros_like(combined)
    #l_binary = np.zeros_like(combined)
    
    s_binary[(s > 200) & (s < 255)] = 1
    #h_binary[(h > 20) & (h < 50)] = 1
    #l_binary[(l > 150) & (l < 255)] = 1
    
    color_binary = np.zeros_like(combined)
    color_binary[(s_binary > 0) | (combined > 0)] = 1
    
    #final_binary = np.zeros_like(color_binary)
    #final_binary[((h_binary > 0) & (l_binary > 0)) | (combined > 0)] = 1
    #final_binary[((h_binary > 0) & (s_binary > 0)) | (combined > 0)] = 1
   
    return warped, M, Minv, color_binary, undist
In [295]:
# Main pipline code that does the Undistort, threshold, HLS/S-channel

def pipeline4(img):

    ksize = 3 # Choose a larger odd number to smooth gradient measurements
    #img = cv2.resize(img, (720, 405))
    #img = cv2.GaussianBlur(img, (ksize, ksize), 0)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    ret, mtx, dist, rvecs, tvecs = cv2.calibrateCamera(objpts, imgpts, gray.shape[::-1], None, None)

    warped, M, Minv, undist = corners_unwarp(img, mtx, dist)
   
    
    hls = cv2.cvtColor(warped, cv2.COLOR_RGB2HLS)
    
    s = hls[:,:,2]
    h = hls[:,:,0]
    l = hls[:,:,1]
    
        
    #plt.imshow(img)
    ksize = 7
    # Apply each of the thresholding functions
    gradx = abs_sobel_thresh(warped, orient='x', sobel_kernel=ksize, thresh=(10, 250))
    grady = abs_sobel_thresh(warped, orient='y', sobel_kernel=ksize, thresh=(10, 250))
    mag_binary = mag_thresh(warped, sobel_kernel=ksize, mag_thresh=(10, 200))
    #plt.imshow(mag_binary)
    dir_binary = dir_threshold(warped, sobel_kernel=ksize, thresh=(0, np.pi/2))
    
    combined = np.zeros_like(dir_binary)
    combined[((gradx == 1) & (grady == 1)) | ((mag_binary == 1) & (dir_binary == 1))] = 1
    

    s_binary = np.zeros_like(combined)
    h_binary = np.zeros_like(combined)
    l_binary = np.zeros_like(combined)
    
    s_binary[(s > 170) & (s < 255)] = 1
    h_binary[(h > 20) & (h < 50)] = 1
    l_binary[(l > 150) & (l < 255)] = 1
    
    #color_binary = np.zeros_like(combined)
    #color_binary[(s_binary > 0) | (combined > 0)] = 1
    
    
    image_HSV = cv2.cvtColor(warped,cv2.COLOR_RGB2HSV)
    yellow_hsv_low  = np.array([ 0,  100,  100])
    yellow_hsv_high = np.array([ 80, 255, 255])
    white_hsv_low  = np.array([ 0,   0,   160])
    white_hsv_high = np.array([ 255,  80, 255])
    mask_yellow =cv2.inRange(image_HSV, yellow_hsv_low, yellow_hsv_high)
    mask_white = cv2.inRange(image_HSV,white_hsv_low,white_hsv_high)
    mask_lane = cv2.bitwise_or(mask_yellow,mask_white)

    color_binary[(mask_lane > 0) | (combined > 0)] = 1

    return warped, M, Minv, color_binary, undist
In [296]:
# Used to detect the lane curve in detect lanes

def lanecurvature(leftx, lefty, rightx, righty) :
    
    
    # Define conversions in x and y from pixels space to meters
    ym_per_pix = 30/720 # meters per pixel in y dimension
    xm_per_pix = 3.7/700 # meters per pixel in x dimension
    
    # Fit new polynomials to x,y in world space
    left_fit_cr = np.polyfit(ploty*ym_per_pix, leftx*xm_per_pix, 2)
    right_fit_cr = np.polyfit(ploty*ym_per_pix, rightx*xm_per_pix, 2)
    # Calculate the new radii of curvature
    left_curverad = ((1 + (2*left_fit_cr[0]*y_eval*ym_per_pix + left_fit_cr[1])**2)**1.5) / np.absolute(2*left_fit_cr[0])
    right_curverad = ((1 + (2*right_fit_cr[0]*y_eval*ym_per_pix + right_fit_cr[1])**2)**1.5) / np.absolute(2*right_fit_cr[0])
    # Now our radius of curvature is in meters
    print(left_curverad, 'm', right_curverad, 'm')
    # Example values: 632.1 m    626.2 m
    
    return left_curverad, right_curverad
In [297]:
# Lane fit function that does the histogram, sliding window, curvature fitting

def lanefit(binary_warped, img):
    # Assuming you have created a warped binary image called "binary_warped"
    # Take a histogram of the bottom half of the image
    histogram = np.sum(binary_warped[binary_warped.shape[0]//2:,:], axis=0)
    # Create an output image to draw on and  visualize the result
    out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255
    # Find the peak of the left and right halves of the histogram
    # These will be the starting point for the left and right lines
    midpoint = np.int(histogram.shape[0]/2)
    leftx_base = np.argmax(histogram[:midpoint])
    rightx_base = np.argmax(histogram[midpoint:]) + midpoint

    # Choose the number of sliding windows
    nwindows = 9
    # Set height of windows
    window_height = np.int(binary_warped.shape[0]/nwindows)
    # Identify the x and y positions of all nonzero pixels in the image
    nonzero = binary_warped.nonzero()
    nonzeroy = np.array(nonzero[0])
    nonzerox = np.array(nonzero[1])
    # Current positions to be updated for each window
    leftx_current = leftx_base
    rightx_current = rightx_base
    # Set the width of the windows +/- margin
    margin = 100
    # Set minimum number of pixels found to recenter window
    minpix = 50
    # Create empty lists to receive left and right lane pixel indices
    left_lane_inds = []
    right_lane_inds = []

    # Step through the windows one by one
    for window in range(nwindows):
        # Identify window boundaries in x and y (and right and left)
        win_y_low = binary_warped.shape[0] - (window+1)*window_height
        win_y_high = binary_warped.shape[0] - window*window_height
        win_xleft_low = leftx_current - margin
        win_xleft_high = leftx_current + margin
        win_xright_low = rightx_current - margin
        win_xright_high = rightx_current + margin
        # Draw the windows on the visualization image
        cv2.rectangle(out_img,(win_xleft_low,win_y_low),(win_xleft_high,win_y_high),(0,255,0), 2) 
        cv2.rectangle(out_img,(win_xright_low,win_y_low),(win_xright_high,win_y_high),(0,255,0), 2) 
        # Identify the nonzero pixels in x and y within the window
        good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xleft_low) & (nonzerox < win_xleft_high)).nonzero()[0]
        good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xright_low) & (nonzerox < win_xright_high)).nonzero()[0]
        # Append these indices to the lists
        left_lane_inds.append(good_left_inds)
        right_lane_inds.append(good_right_inds)
        # If you found > minpix pixels, recenter next window on their mean position
        if len(good_left_inds) > minpix:
            leftx_current = np.int(np.mean(nonzerox[good_left_inds]))
        if len(good_right_inds) > minpix:        
            rightx_current = np.int(np.mean(nonzerox[good_right_inds]))

    # Concatenate the arrays of indices
    left_lane_inds = np.concatenate(left_lane_inds)
    right_lane_inds = np.concatenate(right_lane_inds)

    # Extract left and right line pixel positions
    leftx = nonzerox[left_lane_inds]
    lefty = nonzeroy[left_lane_inds] 
    rightx = nonzerox[right_lane_inds]
    righty = nonzeroy[right_lane_inds] 

    # Fit a second order polynomial to each
    left_fit = np.polyfit(lefty, leftx, 2)
    right_fit = np.polyfit(righty, rightx, 2)
    
    
    # Generate x and y values for plotting
    ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
    left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
    right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
    

    # Fit new polynomials to x,y in world space
    #left_curverad, right_curverad = lanecurvature(leftx, lefty, rightx, righty)
    
    out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
    out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]
    

    return ploty, left_fitx, right_fitx, out_img
In [298]:
def find_3p_circle_radius(x1,y1,x2,y2,x3,y3):
    
    # source : http://www.intmath.com/applications-differentiation/8-radius-curvature.php
    m1 = (y2-y1)/(x2-x1)
    m2 = (y3-y2)/(x3-x2)
    
    xc = (m1*m2*(y1-y3)+m2*(x1+x2)-m1*(x2+x3))/(2*(m2-m1))
    yc = -(xc-(x1+x2)/2)/m1+(y1+y2)/2
    
    Radius = np.sqrt((x2-xc)*(x2-xc)+(y2-yc)*(y2-yc))
    
    return m1, m2, xc, yc, Radius
In [399]:
# Lane fit function that does the histogram, sliding window, curvature fitting

def lanefit2(binary_warped, img, Minv):
    # Assuming you have created a warped binary image called "binary_warped"
    # Take a histogram of the bottom half of the image
    histogram = np.sum(binary_warped[binary_warped.shape[0]//2:,:], axis=0)
    # Create an output image to draw on and  visualize the result
    out_img = np.dstack((binary_warped, binary_warped, binary_warped))*255
    # Find the peak of the left and right halves of the histogram
    # These will be the starting point for the left and right lines
    midpoint = np.int(histogram.shape[0]/2)
    leftx_base = np.argmax(histogram[:midpoint])
    rightx_base = np.argmax(histogram[midpoint:]) + midpoint

    # Choose the number of sliding windows
    nwindows = 9
    # Set height of windows
    window_height = np.int(binary_warped.shape[0]/nwindows)
    # Identify the x and y positions of all nonzero pixels in the image
    nonzero = binary_warped.nonzero()
    nonzeroy = np.array(nonzero[0])
    nonzerox = np.array(nonzero[1])
    # Current positions to be updated for each window
    leftx_current = leftx_base
    rightx_current = rightx_base
    # Set the width of the windows +/- margin
    margin = 100
    # Set minimum number of pixels found to recenter window
    minpix = 50
    # Create empty lists to receive left and right lane pixel indices
    left_lane_inds = []
    right_lane_inds = []

    # Step through the windows one by one
    for window in range(nwindows):
        # Identify window boundaries in x and y (and right and left)
        win_y_low = binary_warped.shape[0] - (window+1)*window_height
        win_y_high = binary_warped.shape[0] - window*window_height
        win_xleft_low = leftx_current - margin
        win_xleft_high = leftx_current + margin
        win_xright_low = rightx_current - margin
        win_xright_high = rightx_current + margin
        # Draw the windows on the visualization image
        cv2.rectangle(out_img,(win_xleft_low,win_y_low),(win_xleft_high,win_y_high),(0,255,0), 2) 
        cv2.rectangle(out_img,(win_xright_low,win_y_low),(win_xright_high,win_y_high),(0,255,0), 2) 
        # Identify the nonzero pixels in x and y within the window
        good_left_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xleft_low) & (nonzerox < win_xleft_high)).nonzero()[0]
        good_right_inds = ((nonzeroy >= win_y_low) & (nonzeroy < win_y_high) & (nonzerox >= win_xright_low) & (nonzerox < win_xright_high)).nonzero()[0]
        # Append these indices to the lists
        left_lane_inds.append(good_left_inds)
        right_lane_inds.append(good_right_inds)
        # If you found > minpix pixels, recenter next window on their mean position
        if len(good_left_inds) > minpix:
            leftx_current = np.int(np.mean(nonzerox[good_left_inds]))
        if len(good_right_inds) > minpix:        
            rightx_current = np.int(np.mean(nonzerox[good_right_inds]))

    # Concatenate the arrays of indices
    left_lane_inds = np.concatenate(left_lane_inds)
    right_lane_inds = np.concatenate(right_lane_inds)

    # Extract left and right line pixel positions
    leftx = nonzerox[left_lane_inds]
    lefty = nonzeroy[left_lane_inds] 
    rightx = nonzerox[right_lane_inds]
    righty = nonzeroy[right_lane_inds] 

    # Fit a second order polynomial to each
    left_fit = np.polyfit(lefty, leftx, 2)
    right_fit = np.polyfit(righty, rightx, 2)
    
    
    # Generate x and y values for plotting
    ploty = np.linspace(0, binary_warped.shape[0]-1, binary_warped.shape[0] )
    left_fitx = left_fit[0]*ploty**2 + left_fit[1]*ploty + left_fit[2]
    right_fitx = right_fit[0]*ploty**2 + right_fit[1]*ploty + right_fit[2]
    

    # Fit new polynomials to x,y in world space

    out_img[nonzeroy[left_lane_inds], nonzerox[left_lane_inds]] = [255, 0, 0]
    out_img[nonzeroy[right_lane_inds], nonzerox[right_lane_inds]] = [0, 0, 255]
    
    """
    New code
    """
    
    # Define conversions in x and y from pixels space to meters
    ym_per_pix = 30/720 # meters per pixel in y dimension
    xm_per_pix = 3.7/700 # meters per pixel in x dimension
    
    # Used 3 y values for max, mean and min
    y_eval_max = np.max(ploty)
    y_eval_mean = np.mean(ploty)
    y_eval_min = np.min(ploty)
    
    left_fitx_1 = left_fit[0]*y_eval_max**2 + left_fit[1]*ploty + left_fit[2]
    left_fitx_2 = left_fit[0]*y_eval_mean**2 + left_fit[1]*ploty + left_fit[2]
    left_fitx_3 = left_fit[0]*y_eval_min**2 + left_fit[1]*ploty + left_fit[2]
    
    right_fitx_1 = right_fit[0]*y_eval_max**2 + right_fit[1]*ploty + right_fit[2]
    right_fitx_2 = right_fit[0]*y_eval_mean**2 + right_fit[1]*ploty + right_fit[2]
    right_fitx_3 = right_fit[0]*y_eval_min**2 + right_fit[1]*ploty + right_fit[2]
    
    
    lm1, lm2, lxc, lyc, lradius = find_3p_circle_radius(left_fitx_1,y_eval_max,left_fitx_2,y_eval_mean,left_fitx_3,y_eval_min,)
    l_steering_angle = 5*360/lxc # assume xc <> 0, xc and radius value is very close, xc will show the direction as well
    
    
    rm1, rm2, rxc, ryc, rradius = find_3p_circle_radius(right_fitx_1,y_eval_max,right_fitx_2,y_eval_mean,right_fitx_3,y_eval_min,)
    
    r_steering_angle = 5*360/rxc # assume xc <> 0, xc and radius value is very close, xc will show the direction as well
    steering_angle = l_steering_angle + r_steering_angle
    turning_radius = (lradius+rradius)/2 # smooth out the radius
    
    # Find camera position
    left_mean = np.mean(leftx)
    right_mean = np.mean(rightx)
    camera_pos = (combined.shape[1]/2)-np.mean([left_mean, right_mean])
    
    # Fit new polynomials to x,y in world space
    #left_fit_cr = np.polyfit(ploty*ym_per_pix, leftx*xm_per_pix, 2)
    #right_fit_cr = np.polyfit(ploty*ym_per_pix, rightx*xm_per_pix, 2)
    
    
    left_fit_cr = np.polyfit(np.array(lefty,dtype=np.float32)*ym_per_pix, \
                         np.array(leftx,dtype=np.float32)*xm_per_pix, 2)
    right_fit_cr = np.polyfit(np.array(righty,dtype=np.float32)*ym_per_pix, \
                          np.array(rightx,dtype=np.float32)*xm_per_pix, 2)
    
    # Calculate the new radii of curvature
    left_curverad = ((1 + (2*left_fit_cr[0]*y_eval_max*ym_per_pix + left_fit_cr[1])**2)**1.5) / np.absolute(2*left_fit_cr[0])
    right_curverad = ((1 + (2*right_fit_cr[0]*y_eval_max*ym_per_pix + right_fit_cr[1])**2)**1.5) / np.absolute(2*right_fit_cr[0])
    # Now our radius of curvature is in meters
    print(left_curverad, 'm', right_curverad, 'm')
    # Example values: 632.1 m    626.2 m
    
    
    """
    New Code end
    """
    warp_zero = np.zeros_like(binary_warped).astype(np.uint8)
    #plt.imshow(warped)
    warp_zero = np.zeros_like(binary_warped).astype(np.uint8)
    color_warp = np.dstack((warp_zero, warp_zero, warp_zero))
    # Recast the x and y points into usable format for cv2.fillPoly()
    pts_left = np.array([np.transpose(np.vstack([left_fitx, ploty]))])
    pts_right = np.array([np.flipud(np.transpose(np.vstack([right_fitx, ploty])))])
    pts = np.hstack((pts_left, pts_right))
        # Draw the lane onto the warped blank image
    cv2.fillPoly(color_warp, np.int_([pts]), (0,255, 0))
    
    font = cv2.FONT_HERSHEY_SIMPLEX
    cv2.putText(img,'Camera Position' + ' [' + str(camera_pos*xm_per_pix)[:6] + '] m',(10,30), font, 1,(255,255,255),2)
    cv2.putText(img,'Turning Radius ' +str(turning_radius)[:6] + '] m' ,(10,60), font, 1,(255,255,255),2)
    
    cv2.putText(img,'Steering Angle -VE Left, +VE Right'+'{:.6}'.format(str(abs(steering_angle))) + '] deg',(10,90), font, 1,(255,255,255),2)
 
    
    # Warp the blank back to original image space using inverse perspective matrix (Minv)
    newwarp = cv2.warpPerspective(color_warp, Minv, (img.shape[1], img.shape[0])) 
    # Combine the result with the original image
    
    result = cv2.addWeighted(img, 1.0, newwarp, 0.3, 0)
    
    return result
In [400]:
# final Image does takes the threshold perspective image, lane fitting and 
# generates the final image with inverse perspective transform after fitting the green polygon

def finalImage(warped, img, ploty, left_fitx, right_fitx, Minv):

    warp_zero = np.zeros_like(warped).astype(np.uint8)
    #plt.imshow(warped)
    warp_zero = np.zeros_like(warped).astype(np.uint8)
    color_warp = np.dstack((warp_zero, warp_zero, warp_zero))
    # Recast the x and y points into usable format for cv2.fillPoly()
    pts_left = np.array([np.transpose(np.vstack([left_fitx, ploty]))])
    pts_right = np.array([np.flipud(np.transpose(np.vstack([right_fitx, ploty])))])
    pts = np.hstack((pts_left, pts_right))
        # Draw the lane onto the warped blank image
    cv2.fillPoly(color_warp, np.int_([pts]), (0,255, 0))
    
    # Warp the blank back to original image space using inverse perspective matrix (Minv)
    newwarp = cv2.warpPerspective(color_warp, Minv, (img.shape[1], img.shape[0])) 
    # Combine the result with the original image
    result = cv2.addWeighted(img, 1.0, newwarp, 0.3, 0)
    return result
In [401]:
img = mpimg.imread('test/23.jpg')
#plt.imshow(img)
warped, M, Minv, binary_warped, undist = pipeline3(img)
#plt.imshow(color_binary)
Image shape: (720, 1280, 3)
In [402]:
plt.imshow(binary_warped, cmap='gray')
Out[402]:
<matplotlib.image.AxesImage at 0x1c2c1fc88>
In [403]:
# Original image and S channel combined with Perspective view

f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(warped)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(binary_warped, cmap='gray')
ax2.set_title('S channel + Combined Image', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
In [404]:
# Histogram showing the peak points in the lane

hist = np.sum(binary_warped[binary_warped.shape[0]//2:,:], axis=0)
plt.plot(hist)
Out[404]:
[<matplotlib.lines.Line2D at 0x1bb8816a0>]
In [405]:
# Test perspective image

warped2, M, Minv, undist = corners_unwarp(img, mtx, dist)
plt.imshow(warped2)
Image shape: (720, 1280, 3)
Out[405]:
<matplotlib.image.AxesImage at 0x184449400>
In [406]:
# Test lanefit

ploty, left_fitx, right_fitx, out_img = lanefit(binary_warped, img)
In [407]:
# Test final Image

result = finalImage(binary_warped, img, ploty, left_fitx, right_fitx, Minv)
plt.imshow(result)
Out[407]:
<matplotlib.image.AxesImage at 0x18a75c128>
In [408]:
# This is main function, that gets the original image and process
# Pipeline, lanefit and final processing 

def process_image(img):
    warped, M, Minv, binary_warped, undist = pipeline3(img)
    ploty, left_fitx, right_fitx, out_img = lanefit(binary_warped, img)
    result = finalImage(binary_warped, img, ploty, left_fitx, right_fitx, Minv)
    return result
    
In [409]:
img2 = mpimg.imread('test_images/test1.jpg')
result2 = process_image(img2)
plt.imshow(result2)
Image shape: (720, 1280, 3)
Out[409]:
<matplotlib.image.AxesImage at 0x184488048>
In [410]:
# Showing the original image and Final Image image for a test image

f, (ax1, ax2) = plt.subplots(1, 2, figsize=(24, 9))
f.tight_layout()
ax1.imshow(img2)
ax1.set_title('Original Image', fontsize=50)
ax2.imshow(result2, cmap='gray')
ax2.set_title('Final Image', fontsize=50)
plt.subplots_adjust(left=0., right=1, top=0.9, bottom=0.)
In [411]:
img2 = mpimg.imread('test/23.jpg')
plt.imshow(img2)
Out[411]:
<matplotlib.image.AxesImage at 0x146578668>
In [412]:
def process_image2(img) :
    
    warped, M, Minv, binary_warped, undist = pipeline3(img)
    result = lanefit2(binary_warped, img, Minv)
   
    return result
In [413]:
result = process_image2(img2)
plt.imshow(result)
Image shape: (720, 1280, 3)
215.9293108 m 358.129306769 m
Out[413]:
<matplotlib.image.AxesImage at 0x1ca25be80>
In [414]:
# Function to read the video file from repository
# Generate the pipeline video file

clip = VideoFileClip("project_video.mp4")
video_out = "project_video_out5.mp4"

video_cap = clip.fl_image(process_image2)
%time video_cap.write_videofile(video_out, audio=False)
Image shape: (720, 1280, 3)
559.349731412 m 2918.86300286 m
[MoviePy] >>>> Building video project_video_out5.mp4
[MoviePy] Writing video project_video_out5.mp4
  0%|          | 0/1261 [00:00<?, ?it/s]
Image shape: (720, 1280, 3)
  0%|          | 1/1261 [00:00<07:37,  2.76it/s]
559.349731412 m 2918.86300286 m
Image shape: (720, 1280, 3)
  0%|          | 2/1261 [00:00<07:28,  2.80it/s]
557.536980085 m 2845.7430005 m
Image shape: (720, 1280, 3)
  0%|          | 3/1261 [00:01<07:16,  2.88it/s]
546.833562691 m 453.844164214 m
Image shape: (720, 1280, 3)
  0%|          | 4/1261 [00:01<07:08,  2.93it/s]
514.789413272 m 712.540269219 m
Image shape: (720, 1280, 3)
  0%|          | 5/1261 [00:01<07:05,  2.95it/s]
508.586462306 m 780.316658662 m
Image shape: (720, 1280, 3)
  0%|          | 6/1261 [00:02<07:03,  2.96it/s]
493.129659883 m 786.025743182 m
Image shape: (720, 1280, 3)
  1%|          | 7/1261 [00:02<07:03,  2.96it/s]
500.040479838 m 744.534630287 m
Image shape: (720, 1280, 3)
  1%|          | 8/1261 [00:02<07:02,  2.96it/s]
509.586051957 m 723.683080551 m
Image shape: (720, 1280, 3)
  1%|          | 9/1261 [00:03<07:03,  2.95it/s]
525.749705944 m 761.112962873 m
Image shape: (720, 1280, 3)
  1%|          | 10/1261 [00:03<07:02,  2.96it/s]
548.552590377 m 740.94684361 m
Image shape: (720, 1280, 3)
  1%|          | 11/1261 [00:03<07:01,  2.97it/s]
557.573715722 m 747.03901177 m
Image shape: (720, 1280, 3)
  1%|          | 12/1261 [00:04<07:07,  2.92it/s]
602.783124311 m 1004.64605921 m
Image shape: (720, 1280, 3)
  1%|          | 13/1261 [00:04<07:16,  2.86it/s]
526.949423338 m 722.355873388 m
Image shape: (720, 1280, 3)
  1%|          | 14/1261 [00:04<07:11,  2.89it/s]
557.520135797 m 530.107310094 m
Image shape: (720, 1280, 3)
  1%|          | 15/1261 [00:05<07:13,  2.87it/s]
544.958156344 m 452.609039153 m
Image shape: (720, 1280, 3)
  1%|▏         | 16/1261 [00:05<07:08,  2.91it/s]
593.157474976 m 817.888327557 m
Image shape: (720, 1280, 3)
  1%|▏         | 17/1261 [00:05<07:13,  2.87it/s]
640.404902841 m 1231.2955547 m
Image shape: (720, 1280, 3)
  1%|▏         | 18/1261 [00:06<07:07,  2.90it/s]
733.644783364 m 766.729053045 m
Image shape: (720, 1280, 3)
  2%|▏         | 19/1261 [00:06<07:18,  2.83it/s]
779.744644266 m 640.813463993 m
Image shape: (720, 1280, 3)
  2%|▏         | 20/1261 [00:06<07:30,  2.75it/s]
806.06042649 m 648.673489965 m
Image shape: (720, 1280, 3)
  2%|▏         | 21/1261 [00:07<07:19,  2.82it/s]
769.958628821 m 732.754317923 m
Image shape: (720, 1280, 3)
  2%|▏         | 22/1261 [00:07<07:14,  2.85it/s]
931.210356338 m 880.848338304 m
Image shape: (720, 1280, 3)
  2%|▏         | 23/1261 [00:07<07:14,  2.85it/s]
887.174926129 m 1114.66553554 m
Image shape: (720, 1280, 3)
  2%|▏         | 24/1261 [00:08<07:17,  2.83it/s]
918.859942539 m 958.527735419 m
Image shape: (720, 1280, 3)
  2%|▏         | 25/1261 [00:08<07:20,  2.80it/s]
887.187662292 m 1190.74638966 m
Image shape: (720, 1280, 3)
  2%|▏         | 26/1261 [00:09<07:17,  2.82it/s]
733.261292981 m 327.140580392 m
Image shape: (720, 1280, 3)
  2%|▏         | 27/1261 [00:09<07:08,  2.88it/s]
778.787058816 m 961.404184135 m
Image shape: (720, 1280, 3)
  2%|▏         | 28/1261 [00:09<07:02,  2.92it/s]
730.205931015 m 1710.58697146 m
Image shape: (720, 1280, 3)
  2%|▏         | 29/1261 [00:09<06:54,  2.97it/s]
709.81481046 m 1087.80110414 m
Image shape: (720, 1280, 3)
  2%|▏         | 30/1261 [00:10<06:51,  2.99it/s]
618.302619697 m 756.423036325 m
Image shape: (720, 1280, 3)
  2%|▏         | 31/1261 [00:10<06:50,  3.00it/s]
616.297650492 m 806.659060851 m
Image shape: (720, 1280, 3)
  3%|▎         | 32/1261 [00:10<06:48,  3.01it/s]
592.82873214 m 556.766367766 m
Image shape: (720, 1280, 3)
  3%|▎         | 33/1261 [00:11<06:45,  3.03it/s]
587.361939746 m 609.509051676 m
Image shape: (720, 1280, 3)
  3%|▎         | 34/1261 [00:11<06:43,  3.04it/s]
552.325706045 m 613.428720192 m
Image shape: (720, 1280, 3)
  3%|▎         | 35/1261 [00:11<06:50,  2.99it/s]
539.265554368 m 629.429371937 m
Image shape: (720, 1280, 3)
  3%|▎         | 36/1261 [00:12<06:48,  3.00it/s]
538.050492087 m 606.066342782 m
Image shape: (720, 1280, 3)
  3%|▎         | 37/1261 [00:12<06:47,  3.00it/s]
528.776296167 m 649.62881791 m
Image shape: (720, 1280, 3)
  3%|▎         | 38/1261 [00:12<06:44,  3.02it/s]
453.625246872 m 304.064078402 m
Image shape: (720, 1280, 3)
  3%|▎         | 39/1261 [00:13<06:45,  3.02it/s]
449.676397492 m 270.834690603 m
Image shape: (720, 1280, 3)
  3%|▎         | 40/1261 [00:13<06:43,  3.03it/s]
438.375853507 m 275.906629275 m
Image shape: (720, 1280, 3)
  3%|▎         | 41/1261 [00:14<06:55,  2.94it/s]
435.69931155 m 290.452378793 m
Image shape: (720, 1280, 3)
  3%|▎         | 42/1261 [00:14<06:50,  2.97it/s]
430.049437319 m 381.504198178 m
Image shape: (720, 1280, 3)
  3%|▎         | 43/1261 [00:14<06:52,  2.95it/s]
418.733074446 m 694.059842122 m
Image shape: (720, 1280, 3)
  3%|▎         | 44/1261 [00:15<06:50,  2.97it/s]
446.091514671 m 591.029394872 m
Image shape: (720, 1280, 3)
  4%|▎         | 45/1261 [00:15<06:46,  2.99it/s]
465.997737274 m 593.641515883 m
Image shape: (720, 1280, 3)
  4%|▎         | 46/1261 [00:15<06:48,  2.97it/s]
438.632452375 m 600.300555066 m
Image shape: (720, 1280, 3)
  4%|▎         | 47/1261 [00:16<06:46,  2.98it/s]
463.98656171 m 633.181255277 m
Image shape: (720, 1280, 3)
  4%|▍         | 48/1261 [00:16<06:44,  3.00it/s]
485.35861266 m 651.978664029 m
Image shape: (720, 1280, 3)
  4%|▍         | 49/1261 [00:16<06:44,  3.00it/s]
502.993686164 m 705.867835904 m
Image shape: (720, 1280, 3)
  4%|▍         | 50/1261 [00:17<06:45,  2.99it/s]
502.037151413 m 376.67158617 m
Image shape: (720, 1280, 3)
  4%|▍         | 51/1261 [00:17<06:47,  2.97it/s]
476.401835359 m 387.018440689 m
Image shape: (720, 1280, 3)
  4%|▍         | 52/1261 [00:17<06:44,  2.99it/s]
441.40060007 m 379.547735908 m
Image shape: (720, 1280, 3)
  4%|▍         | 53/1261 [00:18<06:45,  2.98it/s]
475.206723188 m 355.726616156 m
Image shape: (720, 1280, 3)
  4%|▍         | 54/1261 [00:18<06:57,  2.89it/s]
549.588829413 m 410.672698788 m
Image shape: (720, 1280, 3)
  4%|▍         | 55/1261 [00:18<06:58,  2.88it/s]
547.064748554 m 698.837284023 m
Image shape: (720, 1280, 3)
  4%|▍         | 56/1261 [00:19<06:55,  2.90it/s]
585.686300535 m 822.537476689 m
Image shape: (720, 1280, 3)
  5%|▍         | 57/1261 [00:19<06:49,  2.94it/s]
618.738343661 m 538.181619031 m
Image shape: (720, 1280, 3)
  5%|▍         | 58/1261 [00:19<06:44,  2.98it/s]
834.113527916 m 546.509306054 m
Image shape: (720, 1280, 3)
  5%|▍         | 59/1261 [00:20<07:02,  2.84it/s]
835.059584374 m 500.820129447 m
Image shape: (720, 1280, 3)
  5%|▍         | 60/1261 [00:20<07:08,  2.80it/s]
894.247873013 m 454.827590168 m
Image shape: (720, 1280, 3)
  5%|▍         | 61/1261 [00:20<06:54,  2.89it/s]
908.729966412 m 489.182512114 m
Image shape: (720, 1280, 3)
  5%|▍         | 62/1261 [00:21<06:56,  2.88it/s]
1021.15666316 m 446.609796289 m
Image shape: (720, 1280, 3)
  5%|▍         | 63/1261 [00:21<06:59,  2.85it/s]
692.098431279 m 328.57608118 m
Image shape: (720, 1280, 3)
  5%|▌         | 64/1261 [00:21<07:03,  2.83it/s]
755.694626672 m 475.30547422 m
Image shape: (720, 1280, 3)
  5%|▌         | 65/1261 [00:22<06:58,  2.86it/s]
663.082434533 m 554.860183067 m
Image shape: (720, 1280, 3)
  5%|▌         | 66/1261 [00:22<06:55,  2.87it/s]
677.931465186 m 573.8297408 m
Image shape: (720, 1280, 3)
  5%|▌         | 67/1261 [00:22<06:50,  2.91it/s]
573.765238386 m 890.921356391 m
Image shape: (720, 1280, 3)
  5%|▌         | 68/1261 [00:23<06:44,  2.95it/s]
545.264195623 m 957.731227061 m
Image shape: (720, 1280, 3)
  5%|▌         | 69/1261 [00:23<06:39,  2.99it/s]
545.331203968 m 613.281300253 m
Image shape: (720, 1280, 3)
  6%|▌         | 70/1261 [00:23<06:33,  3.02it/s]
530.779978174 m 589.440933133 m
Image shape: (720, 1280, 3)
  6%|▌         | 71/1261 [00:24<06:32,  3.03it/s]
485.099604216 m 560.428931198 m
Image shape: (720, 1280, 3)
  6%|▌         | 72/1261 [00:24<06:30,  3.05it/s]
477.541541253 m 653.002716709 m
Image shape: (720, 1280, 3)
  6%|▌         | 73/1261 [00:24<06:30,  3.04it/s]
535.558091376 m 718.625227067 m
Image shape: (720, 1280, 3)
  6%|▌         | 74/1261 [00:25<06:30,  3.04it/s]
556.718701647 m 545.580676314 m
Image shape: (720, 1280, 3)
  6%|▌         | 75/1261 [00:25<06:29,  3.05it/s]
620.141810757 m 414.59314398 m
Image shape: (720, 1280, 3)
  6%|▌         | 76/1261 [00:25<06:27,  3.06it/s]
591.6746208 m 378.62364375 m
Image shape: (720, 1280, 3)
  6%|▌         | 77/1261 [00:26<06:28,  3.05it/s]
787.675167073 m 380.065311145 m
Image shape: (720, 1280, 3)
  6%|▌         | 78/1261 [00:26<06:27,  3.05it/s]
807.109066164 m 353.842476384 m
Image shape: (720, 1280, 3)
  6%|▋         | 79/1261 [00:26<06:25,  3.07it/s]
1023.7578552 m 395.284550573 m
Image shape: (720, 1280, 3)
  6%|▋         | 80/1261 [00:27<06:24,  3.07it/s]
777.19111832 m 715.90856157 m
Image shape: (720, 1280, 3)
  6%|▋         | 81/1261 [00:27<06:24,  3.07it/s]
1411.19647593 m 662.998554706 m
Image shape: (720, 1280, 3)
  7%|▋         | 82/1261 [00:27<06:26,  3.05it/s]
1996.32971334 m 624.890558655 m
Image shape: (720, 1280, 3)
  7%|▋         | 83/1261 [00:28<06:25,  3.05it/s]
1694.0240274 m 592.602934046 m
Image shape: (720, 1280, 3)
  7%|▋         | 84/1261 [00:28<06:24,  3.06it/s]
1754.17271381 m 744.083874945 m
Image shape: (720, 1280, 3)
  7%|▋         | 85/1261 [00:28<06:24,  3.06it/s]
1680.46320066 m 707.13094921 m
Image shape: (720, 1280, 3)
  7%|▋         | 86/1261 [00:29<06:21,  3.08it/s]
1950.77081033 m 760.583564656 m
Image shape: (720, 1280, 3)
  7%|▋         | 87/1261 [00:29<06:21,  3.08it/s]
1327.06439341 m 619.790523856 m
Image shape: (720, 1280, 3)
  7%|▋         | 88/1261 [00:29<06:38,  2.94it/s]
736.52075218 m 410.932262276 m
Image shape: (720, 1280, 3)
  7%|▋         | 89/1261 [00:30<06:59,  2.79it/s]
906.48209656 m 325.403487169 m
Image shape: (720, 1280, 3)
  7%|▋         | 90/1261 [00:30<06:56,  2.81it/s]
780.022842679 m 443.843171223 m
Image shape: (720, 1280, 3)
  7%|▋         | 91/1261 [00:30<07:00,  2.79it/s]
765.875601778 m 459.135559379 m
Image shape: (720, 1280, 3)
  7%|▋         | 92/1261 [00:31<06:58,  2.79it/s]
734.782509804 m 733.232397924 m
Image shape: (720, 1280, 3)
  7%|▋         | 93/1261 [00:31<07:02,  2.76it/s]
850.939304326 m 704.783901927 m
Image shape: (720, 1280, 3)
  7%|▋         | 94/1261 [00:31<06:58,  2.79it/s]
589.673848166 m 723.61180034 m
Image shape: (720, 1280, 3)
  8%|▊         | 95/1261 [00:32<06:52,  2.83it/s]
616.544704166 m 688.387114855 m
Image shape: (720, 1280, 3)
  8%|▊         | 96/1261 [00:32<06:45,  2.87it/s]
737.906861446 m 591.222628363 m
Image shape: (720, 1280, 3)
  8%|▊         | 97/1261 [00:33<06:42,  2.89it/s]
563.950620852 m 551.850969587 m
Image shape: (720, 1280, 3)
  8%|▊         | 98/1261 [00:33<06:44,  2.87it/s]
630.623921783 m 584.040057551 m
Image shape: (720, 1280, 3)
  8%|▊         | 99/1261 [00:33<06:50,  2.83it/s]
652.888587501 m 538.030551641 m
Image shape: (720, 1280, 3)
  8%|▊         | 100/1261 [00:34<06:50,  2.83it/s]
645.818897915 m 425.543591963 m
Image shape: (720, 1280, 3)
  8%|▊         | 101/1261 [00:34<06:49,  2.83it/s]
699.44541594 m 424.568960477 m
Image shape: (720, 1280, 3)
  8%|▊         | 102/1261 [00:34<06:49,  2.83it/s]
673.100292499 m 534.898265794 m
Image shape: (720, 1280, 3)
  8%|▊         | 103/1261 [00:35<06:40,  2.89it/s]
667.509951259 m 320.825160346 m
Image shape: (720, 1280, 3)
  8%|▊         | 104/1261 [00:35<06:34,  2.94it/s]
686.842665073 m 518.611017966 m
Image shape: (720, 1280, 3)
  8%|▊         | 105/1261 [00:35<06:31,  2.95it/s]
684.811365748 m 576.034843554 m
Image shape: (720, 1280, 3)
  8%|▊         | 106/1261 [00:36<06:29,  2.96it/s]
739.463389203 m 593.071635003 m
Image shape: (720, 1280, 3)
  8%|▊         | 107/1261 [00:36<06:26,  2.99it/s]
745.885278024 m 1119.77867683 m
Image shape: (720, 1280, 3)
  9%|▊         | 108/1261 [00:36<06:24,  3.00it/s]
677.076847709 m 1006.53168792 m
Image shape: (720, 1280, 3)
  9%|▊         | 109/1261 [00:37<06:24,  3.00it/s]
661.993828847 m 1113.87685079 m
Image shape: (720, 1280, 3)
  9%|▊         | 110/1261 [00:37<06:24,  2.99it/s]
652.841313339 m 1165.09948884 m
Image shape: (720, 1280, 3)
  9%|▉         | 111/1261 [00:37<06:22,  3.00it/s]
660.85671356 m 920.650052098 m
Image shape: (720, 1280, 3)
  9%|▉         | 112/1261 [00:38<06:21,  3.01it/s]
708.045354339 m 489.620439449 m
Image shape: (720, 1280, 3)
  9%|▉         | 113/1261 [00:38<06:22,  3.00it/s]
650.460212789 m 465.42473225 m
Image shape: (720, 1280, 3)
  9%|▉         | 114/1261 [00:38<06:18,  3.03it/s]
626.584138217 m 1914.67464355 m
Image shape: (720, 1280, 3)
  9%|▉         | 115/1261 [00:39<06:18,  3.02it/s]
668.40804692 m 376.243119823 m
Image shape: (720, 1280, 3)
  9%|▉         | 116/1261 [00:39<06:17,  3.03it/s]
566.5026631 m 461.774196911 m
Image shape: (720, 1280, 3)
  9%|▉         | 117/1261 [00:39<06:17,  3.03it/s]
566.529839886 m 352.610993175 m
Image shape: (720, 1280, 3)
  9%|▉         | 118/1261 [00:40<06:21,  3.00it/s]
534.658682013 m 589.176671152 m
Image shape: (720, 1280, 3)
  9%|▉         | 119/1261 [00:40<06:24,  2.97it/s]
542.555567516 m 502.218672734 m
Image shape: (720, 1280, 3)
 10%|▉         | 120/1261 [00:40<06:20,  3.00it/s]
514.513552741 m 474.318888688 m
Image shape: (720, 1280, 3)
 10%|▉         | 121/1261 [00:41<06:24,  2.97it/s]
549.557536404 m 536.82973435 m
Image shape: (720, 1280, 3)
 10%|▉         | 122/1261 [00:41<06:27,  2.94it/s]
522.618509854 m 558.521942032 m
Image shape: (720, 1280, 3)
 10%|▉         | 123/1261 [00:41<06:43,  2.82it/s]
522.494744079 m 472.180742212 m
Image shape: (720, 1280, 3)
 10%|▉         | 124/1261 [00:42<06:36,  2.87it/s]
525.626627017 m 476.99892209 m
Image shape: (720, 1280, 3)
 10%|▉         | 125/1261 [00:42<06:40,  2.83it/s]
523.998198428 m 306.00004139 m
Image shape: (720, 1280, 3)
 10%|▉         | 126/1261 [00:42<06:39,  2.84it/s]
545.471152801 m 323.516774205 m
Image shape: (720, 1280, 3)
 10%|█         | 127/1261 [00:43<06:33,  2.88it/s]
542.351571363 m 344.710904006 m
Image shape: (720, 1280, 3)
 10%|█         | 128/1261 [00:43<06:31,  2.89it/s]
549.003540119 m 406.141931627 m
Image shape: (720, 1280, 3)
 10%|█         | 129/1261 [00:43<06:25,  2.93it/s]
529.808213781 m 358.294729658 m
Image shape: (720, 1280, 3)
 10%|█         | 130/1261 [00:44<06:24,  2.94it/s]
538.892749952 m 351.838950193 m
Image shape: (720, 1280, 3)
 10%|█         | 131/1261 [00:44<06:23,  2.95it/s]
612.567806857 m 521.14861343 m
Image shape: (720, 1280, 3)
 10%|█         | 132/1261 [00:44<06:26,  2.92it/s]
621.709194904 m 510.181068901 m
Image shape: (720, 1280, 3)
 11%|█         | 133/1261 [00:45<06:27,  2.91it/s]
690.963063043 m 553.693695206 m
Image shape: (720, 1280, 3)
 11%|█         | 134/1261 [00:45<06:26,  2.92it/s]
653.563206317 m 439.602772036 m
Image shape: (720, 1280, 3)
 11%|█         | 135/1261 [00:45<06:32,  2.87it/s]
666.711391921 m 515.428935759 m
Image shape: (720, 1280, 3)
 11%|█         | 136/1261 [00:46<06:32,  2.87it/s]
727.571316168 m 565.273970974 m
Image shape: (720, 1280, 3)
 11%|█         | 137/1261 [00:46<06:27,  2.90it/s]
749.741512817 m 707.893605664 m
Image shape: (720, 1280, 3)
 11%|█         | 138/1261 [00:46<06:26,  2.91it/s]
772.765011723 m 891.11407 m
Image shape: (720, 1280, 3)
 11%|█         | 139/1261 [00:47<06:25,  2.91it/s]
749.726400547 m 412.081302145 m
Image shape: (720, 1280, 3)
 11%|█         | 140/1261 [00:47<06:24,  2.91it/s]
721.563569398 m 467.846738761 m
Image shape: (720, 1280, 3)
 11%|█         | 141/1261 [00:48<06:21,  2.94it/s]
747.732088787 m 512.829274323 m
Image shape: (720, 1280, 3)
 11%|█▏        | 142/1261 [00:48<06:15,  2.98it/s]
705.685176338 m 494.507660457 m
Image shape: (720, 1280, 3)
 11%|█▏        | 143/1261 [00:48<06:13,  3.00it/s]
706.372671884 m 874.255620216 m
Image shape: (720, 1280, 3)
 11%|█▏        | 144/1261 [00:49<06:12,  2.99it/s]
697.953308964 m 799.280572602 m
Image shape: (720, 1280, 3)
 11%|█▏        | 145/1261 [00:49<06:17,  2.96it/s]
696.98113976 m 728.390543062 m
Image shape: (720, 1280, 3)
 12%|█▏        | 146/1261 [00:49<06:18,  2.95it/s]
716.611550415 m 614.055075535 m
Image shape: (720, 1280, 3)
 12%|█▏        | 147/1261 [00:50<06:18,  2.94it/s]
717.086289423 m 800.6367935 m
Image shape: (720, 1280, 3)
 12%|█▏        | 148/1261 [00:50<06:18,  2.94it/s]
705.231473519 m 625.881220987 m
Image shape: (720, 1280, 3)
 12%|█▏        | 149/1261 [00:50<06:17,  2.95it/s]
725.107245393 m 706.94625567 m
Image shape: (720, 1280, 3)
 12%|█▏        | 150/1261 [00:51<06:25,  2.88it/s]
764.019440739 m 749.625039309 m
Image shape: (720, 1280, 3)
 12%|█▏        | 151/1261 [00:51<06:28,  2.86it/s]
704.934723871 m 380.602844666 m
Image shape: (720, 1280, 3)
 12%|█▏        | 152/1261 [00:51<06:24,  2.89it/s]
754.707964952 m 311.543951919 m
Image shape: (720, 1280, 3)
 12%|█▏        | 153/1261 [00:52<06:20,  2.91it/s]
716.228884568 m 310.293736775 m
Image shape: (720, 1280, 3)
 12%|█▏        | 154/1261 [00:52<06:14,  2.96it/s]
759.570413344 m 328.751825808 m
Image shape: (720, 1280, 3)
 12%|█▏        | 155/1261 [00:52<06:13,  2.96it/s]
817.006995447 m 439.336411476 m
Image shape: (720, 1280, 3)
 12%|█▏        | 156/1261 [00:53<06:10,  2.98it/s]
799.637430556 m 890.071436258 m
Image shape: (720, 1280, 3)
 12%|█▏        | 157/1261 [00:53<06:07,  3.00it/s]
729.601173913 m 736.584781556 m
Image shape: (720, 1280, 3)
 13%|█▎        | 158/1261 [00:53<06:06,  3.01it/s]
743.062103804 m 608.616137045 m
Image shape: (720, 1280, 3)
 13%|█▎        | 159/1261 [00:54<06:06,  3.01it/s]
583.24805868 m 695.816055027 m
Image shape: (720, 1280, 3)
 13%|█▎        | 160/1261 [00:54<06:03,  3.03it/s]
554.228936946 m 649.619986417 m
Image shape: (720, 1280, 3)
 13%|█▎        | 161/1261 [00:54<06:01,  3.04it/s]
647.855098911 m 732.887202188 m
Image shape: (720, 1280, 3)
 13%|█▎        | 162/1261 [00:55<06:01,  3.04it/s]
620.388563879 m 833.068092383 m
Image shape: (720, 1280, 3)
 13%|█▎        | 163/1261 [00:55<06:01,  3.04it/s]
619.185347946 m 1097.55140193 m
Image shape: (720, 1280, 3)
 13%|█▎        | 164/1261 [00:55<06:01,  3.04it/s]
545.778399286 m 418.384131634 m
Image shape: (720, 1280, 3)
 13%|█▎        | 165/1261 [00:56<06:01,  3.03it/s]
551.695856763 m 436.652479598 m
Image shape: (720, 1280, 3)
 13%|█▎        | 166/1261 [00:56<06:00,  3.04it/s]
602.470754593 m 528.019656983 m
Image shape: (720, 1280, 3)
 13%|█▎        | 167/1261 [00:56<05:58,  3.05it/s]
550.837020829 m 503.183793672 m
Image shape: (720, 1280, 3)
 13%|█▎        | 168/1261 [00:57<05:58,  3.05it/s]
567.636555715 m 924.173088955 m
Image shape: (720, 1280, 3)
 13%|█▎        | 169/1261 [00:57<05:58,  3.05it/s]
602.863615354 m 988.05148778 m
Image shape: (720, 1280, 3)
 13%|█▎        | 170/1261 [00:57<05:57,  3.05it/s]
624.203870541 m 774.595714311 m
Image shape: (720, 1280, 3)
 14%|█▎        | 171/1261 [00:58<06:00,  3.02it/s]
578.710164758 m 930.235078186 m
Image shape: (720, 1280, 3)
 14%|█▎        | 172/1261 [00:58<06:02,  3.00it/s]
559.055074444 m 766.762873351 m
Image shape: (720, 1280, 3)
 14%|█▎        | 173/1261 [00:58<06:00,  3.02it/s]
685.289692786 m 752.059323878 m
Image shape: (720, 1280, 3)
 14%|█▍        | 174/1261 [00:59<05:57,  3.04it/s]
689.178130134 m 692.676649886 m
Image shape: (720, 1280, 3)
 14%|█▍        | 175/1261 [00:59<05:56,  3.04it/s]
691.400880881 m 396.399654919 m
Image shape: (720, 1280, 3)
 14%|█▍        | 176/1261 [00:59<05:57,  3.03it/s]
645.972086846 m 330.292582204 m
Image shape: (720, 1280, 3)
 14%|█▍        | 177/1261 [01:00<05:56,  3.04it/s]
669.983931886 m 324.01727284 m
Image shape: (720, 1280, 3)
 14%|█▍        | 178/1261 [01:00<05:57,  3.03it/s]
698.215189439 m 316.153132889 m
Image shape: (720, 1280, 3)
 14%|█▍        | 179/1261 [01:00<05:55,  3.04it/s]
782.281577003 m 370.591830093 m
Image shape: (720, 1280, 3)
 14%|█▍        | 180/1261 [01:01<05:55,  3.04it/s]
734.407002923 m 473.876560673 m
Image shape: (720, 1280, 3)
 14%|█▍        | 181/1261 [01:01<05:58,  3.01it/s]
726.478610115 m 425.384316682 m
Image shape: (720, 1280, 3)
 14%|█▍        | 182/1261 [01:01<06:02,  2.98it/s]
648.946691604 m 460.879010675 m
Image shape: (720, 1280, 3)
 15%|█▍        | 183/1261 [01:02<05:59,  3.00it/s]
667.108068904 m 441.707426292 m
Image shape: (720, 1280, 3)
 15%|█▍        | 184/1261 [01:02<05:56,  3.02it/s]
525.297532182 m 404.740725952 m
Image shape: (720, 1280, 3)
 15%|█▍        | 185/1261 [01:02<05:55,  3.03it/s]
566.008859405 m 416.364006593 m
Image shape: (720, 1280, 3)
 15%|█▍        | 186/1261 [01:02<05:54,  3.03it/s]
565.579469017 m 312.771885021 m
Image shape: (720, 1280, 3)
 15%|█▍        | 187/1261 [01:03<05:54,  3.03it/s]
438.828099644 m 381.394370043 m
Image shape: (720, 1280, 3)
 15%|█▍        | 188/1261 [01:03<05:54,  3.03it/s]
443.903402377 m 572.466873189 m
Image shape: (720, 1280, 3)
 15%|█▍        | 189/1261 [01:03<05:55,  3.02it/s]
392.620119929 m 300.601388756 m
Image shape: (720, 1280, 3)
 15%|█▌        | 190/1261 [01:04<05:54,  3.02it/s]
417.450720427 m 355.779731429 m
Image shape: (720, 1280, 3)
 15%|█▌        | 191/1261 [01:04<05:54,  3.02it/s]
406.260150209 m 421.030178179 m
Image shape: (720, 1280, 3)
 15%|█▌        | 192/1261 [01:04<05:53,  3.02it/s]
415.117410432 m 513.168965812 m
Image shape: (720, 1280, 3)
 15%|█▌        | 193/1261 [01:05<05:52,  3.03it/s]
429.359502581 m 507.656665325 m
Image shape: (720, 1280, 3)
 15%|█▌        | 194/1261 [01:05<05:55,  3.00it/s]
479.144637655 m 443.06470563 m
Image shape: (720, 1280, 3)
 15%|█▌        | 195/1261 [01:05<05:54,  3.01it/s]
496.388887894 m 383.288665314 m
Image shape: (720, 1280, 3)
 16%|█▌        | 196/1261 [01:06<05:51,  3.03it/s]
519.773957357 m 434.812489713 m
Image shape: (720, 1280, 3)
 16%|█▌        | 197/1261 [01:06<05:50,  3.04it/s]
558.637955196 m 653.595431047 m
Image shape: (720, 1280, 3)
 16%|█▌        | 198/1261 [01:06<05:49,  3.04it/s]
551.95487052 m 1656.44503218 m
Image shape: (720, 1280, 3)
 16%|█▌        | 199/1261 [01:07<05:48,  3.05it/s]
656.475038291 m 1981.23543424 m
Image shape: (720, 1280, 3)
 16%|█▌        | 200/1261 [01:07<05:48,  3.05it/s]
671.255354394 m 2814.4002883 m
Image shape: (720, 1280, 3)
 16%|█▌        | 201/1261 [01:07<05:49,  3.03it/s]
675.868181828 m 568.049749862 m
Image shape: (720, 1280, 3)
 16%|█▌        | 202/1261 [01:08<05:52,  3.01it/s]
648.973141255 m 487.405594089 m
Image shape: (720, 1280, 3)
 16%|█▌        | 203/1261 [01:08<05:53,  2.99it/s]
662.973801247 m 392.664785426 m
Image shape: (720, 1280, 3)
 16%|█▌        | 204/1261 [01:08<05:50,  3.02it/s]
643.938614004 m 466.263209372 m
Image shape: (720, 1280, 3)
 16%|█▋        | 205/1261 [01:09<05:50,  3.01it/s]
637.418453796 m 675.575613329 m
Image shape: (720, 1280, 3)
 16%|█▋        | 206/1261 [01:09<05:51,  3.00it/s]
638.025312141 m 661.399676262 m
Image shape: (720, 1280, 3)
 16%|█▋        | 207/1261 [01:09<05:54,  2.97it/s]
573.398183482 m 513.245643218 m
Image shape: (720, 1280, 3)
 16%|█▋        | 208/1261 [01:10<05:50,  3.00it/s]
548.503882324 m 406.138173613 m
Image shape: (720, 1280, 3)
 17%|█▋        | 209/1261 [01:10<05:48,  3.02it/s]
595.961553365 m 437.565666772 m
Image shape: (720, 1280, 3)
 17%|█▋        | 210/1261 [01:10<05:53,  2.97it/s]
579.958966556 m 545.503940865 m
Image shape: (720, 1280, 3)
 17%|█▋        | 211/1261 [01:11<05:51,  2.99it/s]
594.88765789 m 999.306335992 m
Image shape: (720, 1280, 3)
 17%|█▋        | 212/1261 [01:11<05:49,  3.00it/s]
563.307152027 m 1062.45490661 m
Image shape: (720, 1280, 3)
 17%|█▋        | 213/1261 [01:11<05:52,  2.97it/s]
544.422928317 m 452.527193617 m
Image shape: (720, 1280, 3)
 17%|█▋        | 214/1261 [01:12<05:50,  2.99it/s]
484.033835833 m 336.800089608 m
Image shape: (720, 1280, 3)
 17%|█▋        | 215/1261 [01:12<05:48,  3.01it/s]
457.628582368 m 338.91668544 m
Image shape: (720, 1280, 3)
 17%|█▋        | 216/1261 [01:12<05:45,  3.02it/s]
466.557111499 m 721.097407977 m
Image shape: (720, 1280, 3)
 17%|█▋        | 217/1261 [01:13<05:52,  2.96it/s]
461.392984959 m 776.086602762 m
Image shape: (720, 1280, 3)
 17%|█▋        | 218/1261 [01:13<05:58,  2.91it/s]
494.379682227 m 936.218647994 m
Image shape: (720, 1280, 3)
 17%|█▋        | 219/1261 [01:14<05:57,  2.91it/s]
459.291267118 m 660.732581952 m
Image shape: (720, 1280, 3)
 17%|█▋        | 220/1261 [01:14<05:54,  2.94it/s]
522.516941733 m 646.442146803 m
Image shape: (720, 1280, 3)
 18%|█▊        | 221/1261 [01:14<05:51,  2.96it/s]
510.555224517 m 580.774592037 m
Image shape: (720, 1280, 3)
 18%|█▊        | 222/1261 [01:15<05:54,  2.93it/s]
596.008649233 m 598.387154132 m
Image shape: (720, 1280, 3)
 18%|█▊        | 223/1261 [01:15<05:50,  2.96it/s]
617.412220628 m 649.767397396 m
Image shape: (720, 1280, 3)
 18%|█▊        | 224/1261 [01:15<05:46,  2.99it/s]
605.06664174 m 599.53062271 m
Image shape: (720, 1280, 3)
 18%|█▊        | 225/1261 [01:16<05:45,  3.00it/s]
527.982864181 m 502.187094718 m
Image shape: (720, 1280, 3)
 18%|█▊        | 226/1261 [01:16<05:44,  3.01it/s]
502.5038931 m 468.212087595 m
Image shape: (720, 1280, 3)
 18%|█▊        | 227/1261 [01:16<05:44,  3.00it/s]
597.720724096 m 630.878938626 m
Image shape: (720, 1280, 3)
 18%|█▊        | 228/1261 [01:17<05:43,  3.01it/s]
614.560256332 m 638.056308911 m
Image shape: (720, 1280, 3)
 18%|█▊        | 229/1261 [01:17<05:42,  3.02it/s]
614.334357476 m 1041.47533154 m
Image shape: (720, 1280, 3)
 18%|█▊        | 230/1261 [01:17<05:42,  3.01it/s]
702.697614647 m 940.722001647 m
Image shape: (720, 1280, 3)
 18%|█▊        | 231/1261 [01:17<05:40,  3.02it/s]
744.362973251 m 780.062727864 m
Image shape: (720, 1280, 3)
 18%|█▊        | 232/1261 [01:18<05:49,  2.94it/s]
813.157000047 m 693.0198758 m
Image shape: (720, 1280, 3)
 18%|█▊        | 233/1261 [01:18<05:47,  2.96it/s]
770.539797147 m 629.773794594 m
Image shape: (720, 1280, 3)
 19%|█▊        | 234/1261 [01:19<05:52,  2.91it/s]
800.368699896 m 648.407820058 m
Image shape: (720, 1280, 3)
 19%|█▊        | 235/1261 [01:19<05:50,  2.93it/s]
872.586513089 m 678.182249045 m
Image shape: (720, 1280, 3)
 19%|█▊        | 236/1261 [01:19<05:44,  2.97it/s]
875.223109236 m 616.318785353 m
Image shape: (720, 1280, 3)
 19%|█▉        | 237/1261 [01:20<05:40,  3.01it/s]
588.832531233 m 379.781202797 m
Image shape: (720, 1280, 3)
 19%|█▉        | 238/1261 [01:20<05:39,  3.01it/s]
681.804957662 m 330.182964591 m
Image shape: (720, 1280, 3)
 19%|█▉        | 239/1261 [01:20<05:37,  3.03it/s]
670.202062704 m 319.452898824 m
Image shape: (720, 1280, 3)
 19%|█▉        | 240/1261 [01:21<05:36,  3.04it/s]
743.389523192 m 343.044449772 m
Image shape: (720, 1280, 3)
 19%|█▉        | 241/1261 [01:21<05:37,  3.02it/s]
919.211111595 m 367.504347461 m
Image shape: (720, 1280, 3)
 19%|█▉        | 242/1261 [01:21<05:43,  2.97it/s]
813.323232774 m 576.629167066 m
Image shape: (720, 1280, 3)
 19%|█▉        | 243/1261 [01:22<05:40,  2.99it/s]
803.133304824 m 496.783548521 m
Image shape: (720, 1280, 3)
 19%|█▉        | 244/1261 [01:22<05:38,  3.00it/s]
761.529845447 m 506.962138319 m
Image shape: (720, 1280, 3)
 19%|█▉        | 245/1261 [01:22<05:36,  3.02it/s]
693.100165318 m 427.144354229 m
Image shape: (720, 1280, 3)
 20%|█▉        | 246/1261 [01:23<05:33,  3.04it/s]
751.939966808 m 436.068416628 m
Image shape: (720, 1280, 3)
 20%|█▉        | 247/1261 [01:23<05:32,  3.05it/s]
889.964382241 m 535.939512283 m
Image shape: (720, 1280, 3)
 20%|█▉        | 248/1261 [01:23<05:29,  3.07it/s]
759.698787149 m 486.06809237 m
Image shape: (720, 1280, 3)
 20%|█▉        | 249/1261 [01:23<05:28,  3.08it/s]
582.824172968 m 317.307935237 m
Image shape: (720, 1280, 3)
 20%|█▉        | 250/1261 [01:24<05:28,  3.08it/s]
532.996819167 m 318.698708806 m
Image shape: (720, 1280, 3)
 20%|█▉        | 251/1261 [01:24<05:27,  3.08it/s]
638.203541001 m 323.693228164 m
Image shape: (720, 1280, 3)
 20%|█▉        | 252/1261 [01:24<05:26,  3.09it/s]
639.390947278 m 325.99644681 m
Image shape: (720, 1280, 3)
 20%|██        | 253/1261 [01:25<05:25,  3.09it/s]
737.56601181 m 360.94385528 m
Image shape: (720, 1280, 3)
 20%|██        | 254/1261 [01:25<05:25,  3.09it/s]
592.447625579 m 588.890196415 m
Image shape: (720, 1280, 3)
 20%|██        | 255/1261 [01:25<05:26,  3.08it/s]
787.096811369 m 563.013833318 m
Image shape: (720, 1280, 3)
 20%|██        | 256/1261 [01:26<05:26,  3.08it/s]
737.737479477 m 569.366526342 m
Image shape: (720, 1280, 3)
 20%|██        | 257/1261 [01:26<05:28,  3.06it/s]
685.790257664 m 538.239597198 m
Image shape: (720, 1280, 3)
 20%|██        | 258/1261 [01:26<05:27,  3.07it/s]
719.634701722 m 590.71996421 m
Image shape: (720, 1280, 3)
 21%|██        | 259/1261 [01:27<05:27,  3.06it/s]
808.735501055 m 671.493646638 m
Image shape: (720, 1280, 3)
 21%|██        | 260/1261 [01:27<05:28,  3.05it/s]
801.14342226 m 746.676388078 m
Image shape: (720, 1280, 3)
 21%|██        | 261/1261 [01:27<05:29,  3.04it/s]
780.9878633 m 435.081906113 m
Image shape: (720, 1280, 3)
 21%|██        | 262/1261 [01:28<05:28,  3.04it/s]
782.44771248 m 418.940727282 m
Image shape: (720, 1280, 3)
 21%|██        | 263/1261 [01:28<05:27,  3.05it/s]
879.348509429 m 461.600165876 m
Image shape: (720, 1280, 3)
 21%|██        | 264/1261 [01:28<05:27,  3.04it/s]
926.092466986 m 456.862858543 m
Image shape: (720, 1280, 3)
 21%|██        | 265/1261 [01:29<05:31,  3.01it/s]
1015.94089611 m 495.222795121 m
Image shape: (720, 1280, 3)
 21%|██        | 266/1261 [01:29<05:31,  3.00it/s]
1020.98615366 m 597.133242239 m
Image shape: (720, 1280, 3)
 21%|██        | 267/1261 [01:29<05:29,  3.02it/s]
757.530614027 m 719.99439876 m
Image shape: (720, 1280, 3)
 21%|██▏       | 268/1261 [01:30<05:27,  3.03it/s]
721.870203903 m 739.249117981 m
Image shape: (720, 1280, 3)
 21%|██▏       | 269/1261 [01:30<05:26,  3.04it/s]
838.484868357 m 866.479460804 m
Image shape: (720, 1280, 3)
 21%|██▏       | 270/1261 [01:30<05:24,  3.05it/s]
887.413901981 m 949.862955215 m
Image shape: (720, 1280, 3)
 21%|██▏       | 271/1261 [01:31<05:23,  3.06it/s]
874.933376755 m 992.131918102 m
Image shape: (720, 1280, 3)
 22%|██▏       | 272/1261 [01:31<05:21,  3.08it/s]
910.414700887 m 840.906593682 m
Image shape: (720, 1280, 3)
 22%|██▏       | 273/1261 [01:31<05:21,  3.07it/s]
775.826900451 m 484.421342656 m
Image shape: (720, 1280, 3)
 22%|██▏       | 274/1261 [01:32<05:20,  3.08it/s]
540.521698617 m 577.201499243 m
Image shape: (720, 1280, 3)
 22%|██▏       | 275/1261 [01:32<05:21,  3.06it/s]
601.016362223 m 640.750832217 m
Image shape: (720, 1280, 3)
 22%|██▏       | 276/1261 [01:32<05:21,  3.07it/s]
669.97127773 m 858.065642153 m
Image shape: (720, 1280, 3)
 22%|██▏       | 277/1261 [01:33<05:17,  3.10it/s]
1008.0378586 m 812.495432143 m
Image shape: (720, 1280, 3)
 22%|██▏       | 278/1261 [01:33<05:18,  3.09it/s]
776.485589879 m 845.482828246 m
Image shape: (720, 1280, 3)
 22%|██▏       | 279/1261 [01:33<05:23,  3.04it/s]
569.359618088 m 794.823525089 m
Image shape: (720, 1280, 3)
 22%|██▏       | 280/1261 [01:34<05:25,  3.02it/s]
838.618465885 m 855.695773725 m
Image shape: (720, 1280, 3)
 22%|██▏       | 281/1261 [01:34<05:24,  3.02it/s]
613.670423372 m 876.492283808 m
Image shape: (720, 1280, 3)
 22%|██▏       | 282/1261 [01:34<05:23,  3.03it/s]
678.921768507 m 837.976458446 m
Image shape: (720, 1280, 3)
 22%|██▏       | 283/1261 [01:35<05:20,  3.05it/s]
779.011808139 m 616.002641748 m
Image shape: (720, 1280, 3)
 23%|██▎       | 284/1261 [01:35<05:19,  3.06it/s]
893.338067461 m 599.336095272 m
Image shape: (720, 1280, 3)
 23%|██▎       | 285/1261 [01:35<05:23,  3.01it/s]
764.847767929 m 664.020769202 m
Image shape: (720, 1280, 3)
 23%|██▎       | 286/1261 [01:36<05:24,  3.00it/s]
1055.00385996 m 7071.04414296 m
Image shape: (720, 1280, 3)
 23%|██▎       | 287/1261 [01:36<05:24,  3.00it/s]
605.889341834 m 176376.468789 m
Image shape: (720, 1280, 3)
 23%|██▎       | 288/1261 [01:36<05:23,  3.01it/s]
1340.83116088 m 2249.8048089 m
Image shape: (720, 1280, 3)
 23%|██▎       | 289/1261 [01:37<05:23,  3.01it/s]
989.722158399 m 673.376827371 m
Image shape: (720, 1280, 3)
 23%|██▎       | 290/1261 [01:37<05:22,  3.01it/s]
675.357193152 m 3001.90692739 m
Image shape: (720, 1280, 3)
 23%|██▎       | 291/1261 [01:37<05:21,  3.02it/s]
795.886680541 m 1551.07066866 m
Image shape: (720, 1280, 3)
 23%|██▎       | 292/1261 [01:38<05:21,  3.01it/s]
1771.33026579 m 1762.99267414 m
Image shape: (720, 1280, 3)
 23%|██▎       | 293/1261 [01:38<05:21,  3.01it/s]
823.98519963 m 1982.40764494 m
Image shape: (720, 1280, 3)
 23%|██▎       | 294/1261 [01:38<05:22,  3.00it/s]
915.447402091 m 2409.52777944 m
Image shape: (720, 1280, 3)
 23%|██▎       | 295/1261 [01:39<05:27,  2.95it/s]
1146.01191227 m 730.019553585 m
Image shape: (720, 1280, 3)
 23%|██▎       | 296/1261 [01:39<05:30,  2.92it/s]
1150.41508964 m 677.559049173 m
Image shape: (720, 1280, 3)
 24%|██▎       | 297/1261 [01:39<05:27,  2.95it/s]
1931.24978243 m 736.080679703 m
Image shape: (720, 1280, 3)
 24%|██▎       | 298/1261 [01:40<05:26,  2.95it/s]
1181.83402848 m 11509.3170437 m
Image shape: (720, 1280, 3)
 24%|██▎       | 299/1261 [01:40<05:21,  3.00it/s]
1032.07666636 m 6691.41066637 m
Image shape: (720, 1280, 3)
 24%|██▍       | 300/1261 [01:40<05:17,  3.03it/s]
973.619928046 m 7499.14630262 m
Image shape: (720, 1280, 3)
 24%|██▍       | 301/1261 [01:41<05:17,  3.02it/s]
1106.87141224 m 1161.27618004 m
Image shape: (720, 1280, 3)
 24%|██▍       | 302/1261 [01:41<05:16,  3.03it/s]
1041.10873859 m 1540.22098797 m
Image shape: (720, 1280, 3)
 24%|██▍       | 303/1261 [01:41<05:17,  3.02it/s]
935.166181097 m 2068.70329061 m
Image shape: (720, 1280, 3)
 24%|██▍       | 304/1261 [01:42<05:18,  3.00it/s]
922.991948139 m 2093.68817028 m
Image shape: (720, 1280, 3)
 24%|██▍       | 305/1261 [01:42<05:16,  3.02it/s]
2024.19706808 m 4452.2431581 m
Image shape: (720, 1280, 3)
 24%|██▍       | 306/1261 [01:42<05:15,  3.02it/s]
825.591795916 m 3316.78252816 m
Image shape: (720, 1280, 3)
 24%|██▍       | 307/1261 [01:43<05:12,  3.05it/s]
976.096468727 m 911.932981231 m
Image shape: (720, 1280, 3)
 24%|██▍       | 308/1261 [01:43<05:09,  3.08it/s]
1250.15849421 m 3092.23289572 m
Image shape: (720, 1280, 3)
 25%|██▍       | 309/1261 [01:43<05:07,  3.09it/s]
1652.01392263 m 756.512382396 m
Image shape: (720, 1280, 3)
 25%|██▍       | 310/1261 [01:44<05:08,  3.08it/s]
1211.20714098 m 3460.03032427 m
Image shape: (720, 1280, 3)
 25%|██▍       | 311/1261 [01:44<05:08,  3.08it/s]
6618.5371336 m 1125.08481743 m
Image shape: (720, 1280, 3)
 25%|██▍       | 312/1261 [01:44<05:07,  3.09it/s]
8796.31445146 m 1156.63888788 m
Image shape: (720, 1280, 3)
 25%|██▍       | 313/1261 [01:45<05:06,  3.10it/s]
13933.8827377 m 2876.10223983 m
Image shape: (720, 1280, 3)
 25%|██▍       | 314/1261 [01:45<05:07,  3.08it/s]
37627.6481689 m 6461.45891215 m
Image shape: (720, 1280, 3)
 25%|██▍       | 315/1261 [01:45<05:06,  3.08it/s]
77354.7402482 m 20251.4513043 m
Image shape: (720, 1280, 3)
 25%|██▌       | 316/1261 [01:46<05:06,  3.08it/s]
6787.30487018 m 4231.86618039 m
Image shape: (720, 1280, 3)
 25%|██▌       | 317/1261 [01:46<05:06,  3.08it/s]
8054.99094675 m 1596.66464725 m
Image shape: (720, 1280, 3)
 25%|██▌       | 318/1261 [01:46<05:05,  3.08it/s]
42006.3125311 m 1043.5199197 m
Image shape: (720, 1280, 3)
 25%|██▌       | 319/1261 [01:46<05:04,  3.10it/s]
7115.31479579 m 1104.92739998 m
Image shape: (720, 1280, 3)
 25%|██▌       | 320/1261 [01:47<05:04,  3.09it/s]
3560.22601529 m 1094.29979756 m
Image shape: (720, 1280, 3)
 25%|██▌       | 321/1261 [01:47<05:03,  3.10it/s]
4550.74189459 m 1238.80194773 m
Image shape: (720, 1280, 3)
 26%|██▌       | 322/1261 [01:47<05:05,  3.07it/s]
3887.05594224 m 1585.94714649 m
Image shape: (720, 1280, 3)
 26%|██▌       | 323/1261 [01:48<05:05,  3.07it/s]
4966.4114923 m 21182.752093 m
Image shape: (720, 1280, 3)
 26%|██▌       | 324/1261 [01:48<05:05,  3.06it/s]
4261.40559961 m 7689.21848757 m
Image shape: (720, 1280, 3)
 26%|██▌       | 325/1261 [01:48<05:03,  3.09it/s]
3259.03258491 m 37137.7017191 m
Image shape: (720, 1280, 3)
 26%|██▌       | 326/1261 [01:49<05:03,  3.08it/s]
2617.06860128 m 1306.61221829 m
Image shape: (720, 1280, 3)
 26%|██▌       | 327/1261 [01:49<05:05,  3.06it/s]
2318.82741701 m 1585.16802148 m
Image shape: (720, 1280, 3)
 26%|██▌       | 328/1261 [01:49<05:04,  3.07it/s]
2077.17106773 m 1366.20937495 m
Image shape: (720, 1280, 3)
 26%|██▌       | 329/1261 [01:50<05:04,  3.06it/s]
2008.18186143 m 1784.33503071 m
Image shape: (720, 1280, 3)
 26%|██▌       | 330/1261 [01:50<05:03,  3.07it/s]
1993.57578994 m 988.918104788 m
Image shape: (720, 1280, 3)
 26%|██▌       | 331/1261 [01:50<05:02,  3.08it/s]
2152.06953062 m 875.823275198 m
Image shape: (720, 1280, 3)
 26%|██▋       | 332/1261 [01:51<05:01,  3.08it/s]
1359.98867555 m 3057.78596863 m
Image shape: (720, 1280, 3)
 26%|██▋       | 333/1261 [01:51<05:00,  3.09it/s]
1403.21195278 m 18895.4746378 m
Image shape: (720, 1280, 3)
 26%|██▋       | 334/1261 [01:51<05:00,  3.09it/s]
2022.40493659 m 13952.2372925 m
Image shape: (720, 1280, 3)
 27%|██▋       | 335/1261 [01:52<05:01,  3.07it/s]
2000.2511442 m 3523.51627549 m
Image shape: (720, 1280, 3)
 27%|██▋       | 336/1261 [01:52<05:01,  3.07it/s]
2903.58419763 m 5230.51046014 m
Image shape: (720, 1280, 3)
 27%|██▋       | 337/1261 [01:52<05:01,  3.07it/s]
5579.35246875 m 18200.7028187 m
Image shape: (720, 1280, 3)
 27%|██▋       | 338/1261 [01:53<05:03,  3.04it/s]
3175.1329125 m 2173.29546493 m
Image shape: (720, 1280, 3)
 27%|██▋       | 339/1261 [01:53<05:04,  3.03it/s]
3095.97608402 m 2392.16345906 m
Image shape: (720, 1280, 3)
 27%|██▋       | 340/1261 [01:53<05:01,  3.06it/s]
1701.08676731 m 1752.29303208 m
Image shape: (720, 1280, 3)
 27%|██▋       | 341/1261 [01:54<05:01,  3.05it/s]
2847.22072613 m 1455.89143083 m
Image shape: (720, 1280, 3)
 27%|██▋       | 342/1261 [01:54<05:01,  3.05it/s]
888708.743679 m 828.336381811 m
Image shape: (720, 1280, 3)
 27%|██▋       | 343/1261 [01:54<04:59,  3.07it/s]
3746.24774802 m 20122.977424 m
Image shape: (720, 1280, 3)
 27%|██▋       | 344/1261 [01:55<04:57,  3.09it/s]
8592.62523737 m 1040.49630242 m
Image shape: (720, 1280, 3)
 27%|██▋       | 345/1261 [01:55<04:56,  3.08it/s]
7066.45494725 m 7981.55872294 m
Image shape: (720, 1280, 3)
 27%|██▋       | 346/1261 [01:55<04:54,  3.11it/s]
7656.62428561 m 1573.67583956 m
Image shape: (720, 1280, 3)
 28%|██▊       | 347/1261 [01:56<04:55,  3.10it/s]
3111.82036995 m 4472.61128638 m
Image shape: (720, 1280, 3)
 28%|██▊       | 348/1261 [01:56<04:55,  3.09it/s]
2812.45403473 m 2727.77616378 m
Image shape: (720, 1280, 3)
 28%|██▊       | 349/1261 [01:56<04:56,  3.07it/s]
2375.32852445 m 4646.8199745 m
Image shape: (720, 1280, 3)
 28%|██▊       | 350/1261 [01:57<04:57,  3.06it/s]
1469.47446831 m 7057.07238855 m
Image shape: (720, 1280, 3)
 28%|██▊       | 351/1261 [01:57<04:59,  3.04it/s]
1454.12446117 m 3104.63331512 m
Image shape: (720, 1280, 3)
 28%|██▊       | 352/1261 [01:57<05:06,  2.97it/s]
1590.79302199 m 3045.8009214 m
Image shape: (720, 1280, 3)
 28%|██▊       | 353/1261 [01:58<05:01,  3.01it/s]
1960.23331032 m 3046.78227435 m
Image shape: (720, 1280, 3)
 28%|██▊       | 354/1261 [01:58<04:58,  3.04it/s]
2079.14524814 m 1569.54342963 m
Image shape: (720, 1280, 3)
 28%|██▊       | 355/1261 [01:58<04:57,  3.05it/s]
3168.79096212 m 1705.39063426 m
Image shape: (720, 1280, 3)
 28%|██▊       | 356/1261 [01:59<04:56,  3.05it/s]
4419.70563483 m 1338.61861661 m
Image shape: (720, 1280, 3)
 28%|██▊       | 357/1261 [01:59<04:56,  3.04it/s]
29906.8870785 m 5254.11772417 m
Image shape: (720, 1280, 3)
 28%|██▊       | 358/1261 [01:59<04:58,  3.02it/s]
1922.21554404 m 1536.10751223 m
Image shape: (720, 1280, 3)
 28%|██▊       | 359/1261 [02:00<04:57,  3.03it/s]
5087.01733746 m 2849.18043481 m
Image shape: (720, 1280, 3)
 29%|██▊       | 360/1261 [02:00<04:56,  3.04it/s]
3050.01393057 m 2812.3551216 m
Image shape: (720, 1280, 3)
 29%|██▊       | 361/1261 [02:00<04:54,  3.05it/s]
2269.78121512 m 5036.31035689 m
Image shape: (720, 1280, 3)
 29%|██▊       | 362/1261 [02:01<04:53,  3.06it/s]
1610.49238155 m 5092.17422639 m
Image shape: (720, 1280, 3)
 29%|██▉       | 363/1261 [02:01<04:55,  3.04it/s]
5581.35946468 m 3280.92500815 m
Image shape: (720, 1280, 3)
 29%|██▉       | 364/1261 [02:01<04:55,  3.03it/s]
3775.27639494 m 1379.1097274 m
Image shape: (720, 1280, 3)
 29%|██▉       | 365/1261 [02:02<04:54,  3.04it/s]
2511.25005417 m 3921.01990705 m
Image shape: (720, 1280, 3)
 29%|██▉       | 366/1261 [02:02<04:55,  3.03it/s]
3129.03625645 m 3715.37647319 m
Image shape: (720, 1280, 3)
 29%|██▉       | 367/1261 [02:02<04:57,  3.01it/s]
926.181944581 m 4573.15187348 m
Image shape: (720, 1280, 3)
 29%|██▉       | 368/1261 [02:03<05:00,  2.97it/s]
12746.6572196 m 1035.48053283 m
Image shape: (720, 1280, 3)
 29%|██▉       | 369/1261 [02:03<04:58,  2.99it/s]
2202.21113173 m 10947.3912879 m
Image shape: (720, 1280, 3)
 29%|██▉       | 370/1261 [02:03<05:01,  2.96it/s]
1725.13479806 m 2712.5026543 m
Image shape: (720, 1280, 3)
 29%|██▉       | 371/1261 [02:04<05:03,  2.93it/s]
1094.65497666 m 7496.77395839 m
Image shape: (720, 1280, 3)
 30%|██▉       | 372/1261 [02:04<05:06,  2.90it/s]
1139.9708877 m 2638.32346005 m
Image shape: (720, 1280, 3)
 30%|██▉       | 373/1261 [02:04<05:14,  2.82it/s]
989.849701795 m 2421.01548303 m
Image shape: (720, 1280, 3)
 30%|██▉       | 374/1261 [02:05<05:19,  2.78it/s]
811.284437196 m 108765.038586 m
Image shape: (720, 1280, 3)
 30%|██▉       | 375/1261 [02:05<05:18,  2.78it/s]
683.98051841 m 3300.04304728 m
Image shape: (720, 1280, 3)
 30%|██▉       | 376/1261 [02:05<05:20,  2.76it/s]
842.557307305 m 1468.79902367 m
Image shape: (720, 1280, 3)
 30%|██▉       | 377/1261 [02:06<05:20,  2.76it/s]
854.952527974 m 1324.55319395 m
Image shape: (720, 1280, 3)
 30%|██▉       | 378/1261 [02:06<05:33,  2.65it/s]
1366.92066348 m 1140.34264302 m
Image shape: (720, 1280, 3)
 30%|███       | 379/1261 [02:07<05:45,  2.56it/s]
1047.77720937 m 986.500155806 m
Image shape: (720, 1280, 3)
 30%|███       | 380/1261 [02:07<05:50,  2.51it/s]
1381.89576351 m 1013.53064988 m
Image shape: (720, 1280, 3)
 30%|███       | 381/1261 [02:07<06:06,  2.40it/s]
1648.50964303 m 3169.46075032 m
Image shape: (720, 1280, 3)
 30%|███       | 382/1261 [02:08<05:54,  2.48it/s]
3570.27067245 m 2012.97769961 m
Image shape: (720, 1280, 3)
 30%|███       | 383/1261 [02:08<06:09,  2.38it/s]
4060.25484725 m 3032.43671593 m
Image shape: (720, 1280, 3)
 30%|███       | 384/1261 [02:09<05:56,  2.46it/s]
6859.32576655 m 2659.27233416 m
Image shape: (720, 1280, 3)
 31%|███       | 385/1261 [02:09<05:41,  2.56it/s]
133613.849604 m 3167.02701114 m
Image shape: (720, 1280, 3)
 31%|███       | 386/1261 [02:09<05:25,  2.69it/s]
7284.68467147 m 3713.69923306 m
Image shape: (720, 1280, 3)
 31%|███       | 387/1261 [02:10<05:14,  2.78it/s]
3878.75528072 m 12001.9545371 m
Image shape: (720, 1280, 3)
 31%|███       | 388/1261 [02:10<05:03,  2.87it/s]
5289.25806027 m 3052.77461805 m
Image shape: (720, 1280, 3)
 31%|███       | 389/1261 [02:10<05:14,  2.77it/s]
4482.71129713 m 2581.90590674 m
Image shape: (720, 1280, 3)
 31%|███       | 390/1261 [02:11<05:29,  2.64it/s]
8468.20084533 m 2004.23435403 m
Image shape: (720, 1280, 3)
 31%|███       | 391/1261 [02:11<05:38,  2.57it/s]
3614.65725149 m 1407.8706126 m
Image shape: (720, 1280, 3)
 31%|███       | 392/1261 [02:12<05:46,  2.51it/s]
3470.45094176 m 1209.08821198 m
Image shape: (720, 1280, 3)
 31%|███       | 393/1261 [02:12<05:36,  2.58it/s]
2663.38893523 m 4540.77430596 m
Image shape: (720, 1280, 3)
 31%|███       | 394/1261 [02:12<05:28,  2.64it/s]
13386.3833974 m 1853.90887265 m
Image shape: (720, 1280, 3)
 31%|███▏      | 395/1261 [02:13<05:32,  2.61it/s]
2657.69698808 m 4596.69848883 m
Image shape: (720, 1280, 3)
 31%|███▏      | 396/1261 [02:13<05:30,  2.62it/s]
3743.38791937 m 5920.12014746 m
Image shape: (720, 1280, 3)
 31%|███▏      | 397/1261 [02:14<05:46,  2.49it/s]
3503.13214506 m 4026.71353397 m
Image shape: (720, 1280, 3)
 32%|███▏      | 398/1261 [02:14<05:49,  2.47it/s]
12396.0686245 m 2068.96967013 m
Image shape: (720, 1280, 3)
 32%|███▏      | 399/1261 [02:14<05:40,  2.53it/s]
21868.8130657 m 1949.99946026 m
Image shape: (720, 1280, 3)
 32%|███▏      | 400/1261 [02:15<05:39,  2.54it/s]
31148.8452611 m 11956.2100242 m
Image shape: (720, 1280, 3)
 32%|███▏      | 401/1261 [02:15<05:32,  2.58it/s]
10490.8752442 m 4228.07433419 m
Image shape: (720, 1280, 3)
 32%|███▏      | 402/1261 [02:16<05:36,  2.55it/s]
3957.45768804 m 2190.12518426 m
Image shape: (720, 1280, 3)
 32%|███▏      | 403/1261 [02:16<05:39,  2.53it/s]
3532.68332769 m 1261.00055325 m
Image shape: (720, 1280, 3)
 32%|███▏      | 404/1261 [02:16<06:04,  2.35it/s]
3104.55362362 m 1511.11726282 m
Image shape: (720, 1280, 3)
 32%|███▏      | 405/1261 [02:17<05:59,  2.38it/s]
3021.79893273 m 3461.78241676 m
Image shape: (720, 1280, 3)
 32%|███▏      | 406/1261 [02:17<05:55,  2.40it/s]
2289.8702427 m 1076.76613057 m
Image shape: (720, 1280, 3)
 32%|███▏      | 407/1261 [02:18<05:47,  2.46it/s]
806.226428947 m 1784.32593575 m
Image shape: (720, 1280, 3)
 32%|███▏      | 408/1261 [02:18<05:43,  2.48it/s]
1517.82063266 m 2529.05905432 m
Image shape: (720, 1280, 3)
 32%|███▏      | 409/1261 [02:18<05:44,  2.47it/s]
2142.65730748 m 2934.94249338 m
Image shape: (720, 1280, 3)
 33%|███▎      | 410/1261 [02:19<05:46,  2.46it/s]
1706.60139524 m 13859.6411524 m
Image shape: (720, 1280, 3)
 33%|███▎      | 411/1261 [02:19<05:39,  2.50it/s]
5700.52475057 m 38020.032651 m
Image shape: (720, 1280, 3)
 33%|███▎      | 412/1261 [02:20<05:37,  2.51it/s]
17525.4513971 m 5432.54929478 m
Image shape: (720, 1280, 3)
 33%|███▎      | 413/1261 [02:20<05:34,  2.54it/s]
58635.0947952 m 2912.37015783 m
Image shape: (720, 1280, 3)
 33%|███▎      | 414/1261 [02:20<05:32,  2.54it/s]
4652.92155086 m 2542.34974685 m
Image shape: (720, 1280, 3)
 33%|███▎      | 415/1261 [02:21<06:02,  2.34it/s]
3468.43245845 m 4598.42287903 m
Image shape: (720, 1280, 3)
 33%|███▎      | 416/1261 [02:21<05:58,  2.36it/s]
4191.01341086 m 1528.88846955 m
Image shape: (720, 1280, 3)
 33%|███▎      | 417/1261 [02:22<06:05,  2.31it/s]
12099.2404825 m 708007.023446 m
Image shape: (720, 1280, 3)
 33%|███▎      | 418/1261 [02:22<05:48,  2.42it/s]
10278.8249982 m 1607.63016091 m
Image shape: (720, 1280, 3)
 33%|███▎      | 419/1261 [02:23<05:32,  2.53it/s]
70344.2432191 m 8118.10265374 m
Image shape: (720, 1280, 3)
 33%|███▎      | 420/1261 [02:23<05:22,  2.60it/s]
6427.63082405 m 27288.0867529 m
Image shape: (720, 1280, 3)
 33%|███▎      | 421/1261 [02:23<05:59,  2.34it/s]
17051.921928 m 31302.5225802 m
Image shape: (720, 1280, 3)
 33%|███▎      | 422/1261 [02:24<05:42,  2.45it/s]
2032.91750874 m 98352.018193 m
Image shape: (720, 1280, 3)
 34%|███▎      | 423/1261 [02:24<05:28,  2.55it/s]
4754.04398847 m 11378.1307116 m
Image shape: (720, 1280, 3)
 34%|███▎      | 424/1261 [02:25<05:28,  2.55it/s]
27783.2584471 m 4396.25078923 m
Image shape: (720, 1280, 3)
 34%|███▎      | 425/1261 [02:25<05:17,  2.63it/s]
506612.386578 m 2303.90203593 m
Image shape: (720, 1280, 3)
 34%|███▍      | 426/1261 [02:25<05:07,  2.72it/s]
3119.16951413 m 2932.03404284 m
Image shape: (720, 1280, 3)
 34%|███▍      | 427/1261 [02:26<04:56,  2.81it/s]
7402.75650984 m 1795.85591728 m
Image shape: (720, 1280, 3)
 34%|███▍      | 428/1261 [02:26<04:48,  2.89it/s]
91640.3334114 m 1827.1594541 m
Image shape: (720, 1280, 3)
 34%|███▍      | 429/1261 [02:26<04:41,  2.96it/s]
143949.335 m 1555.58495954 m
Image shape: (720, 1280, 3)
 34%|███▍      | 430/1261 [02:26<04:37,  3.00it/s]
5725.06703391 m 5900.5387312 m
Image shape: (720, 1280, 3)
 34%|███▍      | 431/1261 [02:27<05:10,  2.67it/s]
1423.98565355 m 30084.3210323 m
Image shape: (720, 1280, 3)
 34%|███▍      | 432/1261 [02:27<04:59,  2.76it/s]
4101.32998568 m 3873.37709692 m
Image shape: (720, 1280, 3)
 34%|███▍      | 433/1261 [02:28<05:05,  2.71it/s]
7569.58708655 m 8218.38409169 m
Image shape: (720, 1280, 3)
 34%|███▍      | 434/1261 [02:28<05:01,  2.75it/s]
3182.42969683 m 6302.08859497 m
Image shape: (720, 1280, 3)
 34%|███▍      | 435/1261 [02:28<04:51,  2.83it/s]
11246.6143263 m 4450.02475046 m
Image shape: (720, 1280, 3)
 35%|███▍      | 436/1261 [02:29<04:45,  2.89it/s]
2343.65311141 m 36766.0210553 m
Image shape: (720, 1280, 3)
 35%|███▍      | 437/1261 [02:29<05:16,  2.61it/s]
4135.78816205 m 2817.28516447 m
Image shape: (720, 1280, 3)
 35%|███▍      | 438/1261 [02:30<05:22,  2.56it/s]
7113.54548614 m 2640.29832683 m
Image shape: (720, 1280, 3)
 35%|███▍      | 439/1261 [02:30<05:16,  2.60it/s]
2165.83430126 m 1574.8535617 m
Image shape: (720, 1280, 3)
 35%|███▍      | 440/1261 [02:30<05:06,  2.67it/s]
3927.78781419 m 1431.98508254 m
Image shape: (720, 1280, 3)
 35%|███▍      | 441/1261 [02:31<04:57,  2.75it/s]
3313.56317256 m 4630.05268645 m
Image shape: (720, 1280, 3)
 35%|███▌      | 442/1261 [02:31<04:53,  2.79it/s]
7380.84995429 m 7854.52039496 m
Image shape: (720, 1280, 3)
 35%|███▌      | 443/1261 [02:31<04:47,  2.84it/s]
12076305.2942 m 9823.61148497 m
Image shape: (720, 1280, 3)
 35%|███▌      | 444/1261 [02:32<04:40,  2.91it/s]
6355.43874961 m 2771.22051823 m
Image shape: (720, 1280, 3)
 35%|███▌      | 445/1261 [02:32<04:35,  2.97it/s]
1238.34209163 m 4127.13161637 m
Image shape: (720, 1280, 3)
 35%|███▌      | 446/1261 [02:32<04:33,  2.98it/s]
1882.50809637 m 9765.6954697 m
Image shape: (720, 1280, 3)
 35%|███▌      | 447/1261 [02:33<04:36,  2.95it/s]
1328.88493724 m 4388.61585959 m
Image shape: (720, 1280, 3)
 36%|███▌      | 448/1261 [02:33<04:36,  2.94it/s]
931.819365298 m 2550.48343335 m
Image shape: (720, 1280, 3)
 36%|███▌      | 449/1261 [02:33<04:32,  2.98it/s]
1276.87714413 m 2876.24774812 m
Image shape: (720, 1280, 3)
 36%|███▌      | 450/1261 [02:34<04:31,  2.99it/s]
863.777496512 m 1787.54933611 m
Image shape: (720, 1280, 3)
 36%|███▌      | 451/1261 [02:34<04:29,  3.00it/s]
845.302907024 m 1912.44842524 m
Image shape: (720, 1280, 3)
 36%|███▌      | 452/1261 [02:34<04:27,  3.03it/s]
934.007468815 m 1374.70514572 m
Image shape: (720, 1280, 3)
 36%|███▌      | 453/1261 [02:35<04:25,  3.04it/s]
1184.84826702 m 1211.18404328 m
Image shape: (720, 1280, 3)
 36%|███▌      | 454/1261 [02:35<04:24,  3.05it/s]
6430.03812332 m 3047.34803369 m
Image shape: (720, 1280, 3)
 36%|███▌      | 455/1261 [02:35<04:23,  3.06it/s]
2218.24192238 m 5390.45488347 m
Image shape: (720, 1280, 3)
 36%|███▌      | 456/1261 [02:36<04:22,  3.06it/s]
1558.06331085 m 25529.4794279 m
Image shape: (720, 1280, 3)
 36%|███▌      | 457/1261 [02:36<04:20,  3.08it/s]
895.392629385 m 6952.30760526 m
Image shape: (720, 1280, 3)
 36%|███▋      | 458/1261 [02:36<05:09,  2.60it/s]
1647.08836768 m 6013.3850439 m
Image shape: (720, 1280, 3)
 36%|███▋      | 459/1261 [02:37<05:00,  2.67it/s]
913.333660029 m 3085.00533893 m
Image shape: (720, 1280, 3)
 36%|███▋      | 460/1261 [02:37<04:47,  2.78it/s]
845.202759422 m 3160.04236368 m
Image shape: (720, 1280, 3)
 37%|███▋      | 461/1261 [02:37<04:41,  2.85it/s]
1546.98012295 m 5266.06267196 m
Image shape: (720, 1280, 3)
 37%|███▋      | 462/1261 [02:38<04:35,  2.90it/s]
896.240577799 m 3463.30796581 m
Image shape: (720, 1280, 3)
 37%|███▋      | 463/1261 [02:38<04:32,  2.93it/s]
1205.94944736 m 2373.38674729 m
Image shape: (720, 1280, 3)
 37%|███▋      | 464/1261 [02:38<04:30,  2.95it/s]
1600.9186089 m 1877.72308565 m
Image shape: (720, 1280, 3)
 37%|███▋      | 465/1261 [02:39<04:29,  2.95it/s]
2258.91146149 m 98552.0144245 m
Image shape: (720, 1280, 3)
 37%|███▋      | 466/1261 [02:39<04:27,  2.97it/s]
3188.02477671 m 2160.58084483 m
Image shape: (720, 1280, 3)
 37%|███▋      | 467/1261 [02:39<04:27,  2.97it/s]
3215.60021492 m 4249.40438896 m
Image shape: (720, 1280, 3)
 37%|███▋      | 468/1261 [02:40<05:02,  2.62it/s]
3342.49425565 m 5742.06162706 m
Image shape: (720, 1280, 3)
 37%|███▋      | 469/1261 [02:40<04:53,  2.69it/s]
1252.27710511 m 2850.76097721 m
Image shape: (720, 1280, 3)
 37%|███▋      | 470/1261 [02:41<04:44,  2.78it/s]
2457.24016466 m 1835.70681138 m
Image shape: (720, 1280, 3)
 37%|███▋      | 471/1261 [02:41<04:37,  2.85it/s]
2568.9477046 m 2953.21020044 m
Image shape: (720, 1280, 3)
 37%|███▋      | 472/1261 [02:41<04:34,  2.87it/s]
4917.64918347 m 5985.44322869 m
Image shape: (720, 1280, 3)
 38%|███▊      | 473/1261 [02:42<04:36,  2.85it/s]
2703.0842004 m 10202.1569201 m
Image shape: (720, 1280, 3)
 38%|███▊      | 474/1261 [02:42<04:35,  2.85it/s]
2960.18656173 m 5762.16869192 m
Image shape: (720, 1280, 3)
 38%|███▊      | 475/1261 [02:42<04:34,  2.87it/s]
6117.73492744 m 6305.34390383 m
Image shape: (720, 1280, 3)
 38%|███▊      | 476/1261 [02:43<05:20,  2.45it/s]
17488.6079092 m 4640.36006165 m
Image shape: (720, 1280, 3)
 38%|███▊      | 477/1261 [02:43<05:03,  2.59it/s]
4974.8672949 m 2364.35241744 m
Image shape: (720, 1280, 3)
 38%|███▊      | 478/1261 [02:44<04:59,  2.62it/s]
4756.38281158 m 1624.86649135 m
Image shape: (720, 1280, 3)
 38%|███▊      | 479/1261 [02:44<04:49,  2.71it/s]
3861.77638577 m 18581.9449792 m
Image shape: (720, 1280, 3)
 38%|███▊      | 480/1261 [02:44<04:40,  2.79it/s]
3779.33903166 m 25304695.8696 m
Image shape: (720, 1280, 3)
 38%|███▊      | 481/1261 [02:45<04:35,  2.83it/s]
3486.32099619 m 4321.93957161 m
Image shape: (720, 1280, 3)
 38%|███▊      | 482/1261 [02:45<04:30,  2.88it/s]
11172.180303 m 5879.56036539 m
Image shape: (720, 1280, 3)
 38%|███▊      | 483/1261 [02:45<04:27,  2.91it/s]
4625.12895944 m 2152.57553148 m
Image shape: (720, 1280, 3)
 38%|███▊      | 484/1261 [02:46<04:23,  2.95it/s]
1957.20461789 m 3081.73068494 m
Image shape: (720, 1280, 3)
 38%|███▊      | 485/1261 [02:46<04:20,  2.98it/s]
2946.07108578 m 23336.2724108 m
Image shape: (720, 1280, 3)
 39%|███▊      | 486/1261 [02:46<04:18,  2.99it/s]
2474.86776329 m 1832.96677974 m
Image shape: (720, 1280, 3)
 39%|███▊      | 487/1261 [02:47<04:16,  3.02it/s]
2220.27092662 m 1596.54637608 m
Image shape: (720, 1280, 3)
 39%|███▊      | 488/1261 [02:47<04:14,  3.03it/s]
2209.99261071 m 1310.38749292 m
Image shape: (720, 1280, 3)
 39%|███▉      | 489/1261 [02:47<04:14,  3.04it/s]
175433.391663 m 1228.49380499 m
Image shape: (720, 1280, 3)
 39%|███▉      | 490/1261 [02:48<04:13,  3.04it/s]
3070.93541947 m 4870.87745876 m
Image shape: (720, 1280, 3)
 39%|███▉      | 491/1261 [02:48<04:15,  3.02it/s]
3986.815865 m 8440.83281438 m
Image shape: (720, 1280, 3)
 39%|███▉      | 492/1261 [02:48<04:14,  3.02it/s]
5672.67548877 m 7841.36281114 m
Image shape: (720, 1280, 3)
 39%|███▉      | 493/1261 [02:49<04:15,  3.01it/s]
4956.1429121 m 16159.4943433 m
Image shape: (720, 1280, 3)
 39%|███▉      | 494/1261 [02:49<04:14,  3.01it/s]
4855.79662886 m 26574.243637 m
Image shape: (720, 1280, 3)
 39%|███▉      | 495/1261 [02:49<04:13,  3.02it/s]
4348.93508476 m 14970.4882453 m
Image shape: (720, 1280, 3)
 39%|███▉      | 496/1261 [02:50<04:13,  3.02it/s]
2641.84634844 m 6361.18969242 m
Image shape: (720, 1280, 3)
 39%|███▉      | 497/1261 [02:50<04:17,  2.96it/s]
4102.85470012 m 1913.02003884 m
Image shape: (720, 1280, 3)
 39%|███▉      | 498/1261 [02:50<04:18,  2.95it/s]
3379.94260265 m 1368.64670649 m
Image shape: (720, 1280, 3)
 40%|███▉      | 499/1261 [02:51<04:13,  3.00it/s]
3718.0889921 m 1145.37837194 m
Image shape: (720, 1280, 3)
 40%|███▉      | 500/1261 [02:51<04:12,  3.01it/s]
4248.35986937 m 1020.61997299 m
Image shape: (720, 1280, 3)
 40%|███▉      | 501/1261 [02:51<04:10,  3.03it/s]
13263.4293138 m 1014.64058287 m
Image shape: (720, 1280, 3)
 40%|███▉      | 502/1261 [02:52<04:11,  3.02it/s]
6991.26835156 m 917.76477689 m
Image shape: (720, 1280, 3)
 40%|███▉      | 503/1261 [02:52<04:11,  3.01it/s]
3383.89831103 m 792.880879645 m
Image shape: (720, 1280, 3)
 40%|███▉      | 504/1261 [02:52<04:12,  2.99it/s]
5069.12136413 m 720.762637031 m
Image shape: (720, 1280, 3)
 40%|████      | 505/1261 [02:53<04:21,  2.89it/s]
4888.6407657 m 2055.93663516 m
Image shape: (720, 1280, 3)
 40%|████      | 506/1261 [02:53<04:19,  2.91it/s]
4390.51829059 m 5356.99038232 m
Image shape: (720, 1280, 3)
 40%|████      | 507/1261 [02:53<04:20,  2.90it/s]
5223.9923638 m 3291.27623404 m
Image shape: (720, 1280, 3)
 40%|████      | 508/1261 [02:54<04:18,  2.91it/s]
6107.5621749 m 2358.44304904 m
Image shape: (720, 1280, 3)
 40%|████      | 509/1261 [02:54<04:14,  2.95it/s]
279144.652564 m 2448.12770206 m
Image shape: (720, 1280, 3)
 40%|████      | 510/1261 [02:54<04:12,  2.98it/s]
10230.3098459 m 3788.91258259 m
Image shape: (720, 1280, 3)
 41%|████      | 511/1261 [02:55<04:12,  2.97it/s]
9147.60998438 m 3017.63864136 m
Image shape: (720, 1280, 3)
 41%|████      | 512/1261 [02:55<04:13,  2.96it/s]
8473.06157563 m 2264.83733488 m
Image shape: (720, 1280, 3)
 41%|████      | 513/1261 [02:55<04:13,  2.95it/s]
10416.8038211 m 1794.04787388 m
Image shape: (720, 1280, 3)
 41%|████      | 514/1261 [02:56<04:15,  2.92it/s]
6572.4468107 m 5585.41143864 m
Image shape: (720, 1280, 3)
 41%|████      | 515/1261 [02:56<04:28,  2.78it/s]
4057.89381042 m 8432.41034404 m
Image shape: (720, 1280, 3)
 41%|████      | 516/1261 [02:57<04:46,  2.60it/s]
6474.47694174 m 4327.74030058 m
Image shape: (720, 1280, 3)
 41%|████      | 517/1261 [02:57<04:47,  2.59it/s]
4874.04320923 m 2808.12944565 m
Image shape: (720, 1280, 3)
 41%|████      | 518/1261 [02:57<04:43,  2.62it/s]
5411.06160601 m 3950.39532488 m
Image shape: (720, 1280, 3)
 41%|████      | 519/1261 [02:58<04:37,  2.68it/s]
80431.8365319 m 13181.0985011 m
Image shape: (720, 1280, 3)
 41%|████      | 520/1261 [02:58<04:33,  2.71it/s]
1898.21556028 m 2113584.63462 m
Image shape: (720, 1280, 3)
 41%|████▏     | 521/1261 [02:58<04:28,  2.76it/s]
4873.75959567 m 49053.7900932 m
Image shape: (720, 1280, 3)
 41%|████▏     | 522/1261 [02:59<04:26,  2.77it/s]
4205.11979273 m 3384.59150589 m
Image shape: (720, 1280, 3)
 41%|████▏     | 523/1261 [02:59<04:30,  2.73it/s]
4644.80542559 m 1441.09698003 m
Image shape: (720, 1280, 3)
 42%|████▏     | 524/1261 [03:00<04:54,  2.50it/s]
3358.6629746 m 1137.49527479 m
Image shape: (720, 1280, 3)
 42%|████▏     | 525/1261 [03:00<04:49,  2.55it/s]
5919.0539567 m 1135.78077203 m
Image shape: (720, 1280, 3)
 42%|████▏     | 526/1261 [03:00<04:42,  2.60it/s]
12918.5433632 m 2736.26838601 m
Image shape: (720, 1280, 3)
 42%|████▏     | 527/1261 [03:01<04:41,  2.60it/s]
2193.21196422 m 143350.784779 m
Image shape: (720, 1280, 3)
 42%|████▏     | 528/1261 [03:01<04:43,  2.58it/s]
4385.61608869 m 11075.4386624 m
Image shape: (720, 1280, 3)
 42%|████▏     | 529/1261 [03:01<04:39,  2.62it/s]
1362.2698637 m 4474.82607678 m
Image shape: (720, 1280, 3)
 42%|████▏     | 530/1261 [03:02<04:48,  2.54it/s]
1392.7513219 m 3945.22064931 m
Image shape: (720, 1280, 3)
 42%|████▏     | 531/1261 [03:02<04:53,  2.49it/s]
1204.15028675 m 3653.21075158 m
Image shape: (720, 1280, 3)
 42%|████▏     | 532/1261 [03:03<04:49,  2.52it/s]
1488.35449821 m 3589.27746413 m
Image shape: (720, 1280, 3)
 42%|████▏     | 533/1261 [03:03<04:43,  2.57it/s]
4287.46055057 m 1953.75528716 m
Image shape: (720, 1280, 3)
 42%|████▏     | 534/1261 [03:03<04:49,  2.51it/s]
3341.49417095 m 832.420797319 m
Image shape: (720, 1280, 3)
 42%|████▏     | 535/1261 [03:04<04:44,  2.55it/s]
12945.410248 m 768.382158595 m
Image shape: (720, 1280, 3)
 43%|████▎     | 536/1261 [03:04<04:38,  2.60it/s]
12085.8342642 m 683.110463138 m
Image shape: (720, 1280, 3)
 43%|████▎     | 537/1261 [03:05<04:35,  2.63it/s]
29873.6989114 m 1023.29347007 m
Image shape: (720, 1280, 3)
 43%|████▎     | 538/1261 [03:05<04:40,  2.58it/s]
4902.87694087 m 504.059136487 m
Image shape: (720, 1280, 3)
 43%|████▎     | 539/1261 [03:05<04:42,  2.56it/s]
1028.61407098 m 1559.95281062 m
Image shape: (720, 1280, 3)
 43%|████▎     | 540/1261 [03:06<05:12,  2.31it/s]
903.358533413 m 2303.60329458 m
Image shape: (720, 1280, 3)
 43%|████▎     | 541/1261 [03:06<05:12,  2.30it/s]
4249.6240655 m 2130.88786292 m
Image shape: (720, 1280, 3)
 43%|████▎     | 542/1261 [03:07<05:09,  2.33it/s]
1515.98059253 m 4263.7319357 m
Image shape: (720, 1280, 3)
 43%|████▎     | 543/1261 [03:07<05:09,  2.32it/s]
770.839214367 m 6041.54041227 m
Image shape: (720, 1280, 3)
 43%|████▎     | 544/1261 [03:08<05:07,  2.33it/s]
350.318175369 m 5935.57270989 m
Image shape: (720, 1280, 3)
 43%|████▎     | 545/1261 [03:08<04:56,  2.42it/s]
401.914275499 m 11873.7900857 m
Image shape: (720, 1280, 3)
 43%|████▎     | 546/1261 [03:08<04:50,  2.46it/s]
238.475631784 m 484.773459554 m
Image shape: (720, 1280, 3)
 43%|████▎     | 547/1261 [03:09<04:39,  2.55it/s]
235.482320574 m 387.469478843 m
Image shape: (720, 1280, 3)
 43%|████▎     | 548/1261 [03:09<04:36,  2.58it/s]
2628.57625662 m 459.754489685 m
Image shape: (720, 1280, 3)
 44%|████▎     | 549/1261 [03:10<04:34,  2.59it/s]
1146.51372831 m 599.201097253 m
Image shape: (720, 1280, 3)
 44%|████▎     | 550/1261 [03:10<04:36,  2.57it/s]
419.829481172 m 428.609246059 m
Image shape: (720, 1280, 3)
 44%|████▎     | 551/1261 [03:10<04:45,  2.48it/s]
310.530592626 m 956.111096431 m
Image shape: (720, 1280, 3)
 44%|████▍     | 552/1261 [03:11<04:48,  2.46it/s]
1780.4299832 m 1074.97802157 m
Image shape: (720, 1280, 3)
 44%|████▍     | 553/1261 [03:11<04:57,  2.38it/s]
528.515381529 m 1858.5068641 m
Image shape: (720, 1280, 3)
 44%|████▍     | 554/1261 [03:12<05:03,  2.33it/s]
156.397094244 m 415.166699051 m
Image shape: (720, 1280, 3)
 44%|████▍     | 555/1261 [03:12<04:55,  2.39it/s]
293.115804338 m 468.240769078 m
Image shape: (720, 1280, 3)
 44%|████▍     | 556/1261 [03:12<04:42,  2.49it/s]
1637.33840923 m 706.50832561 m
Image shape: (720, 1280, 3)
 44%|████▍     | 557/1261 [03:13<04:33,  2.57it/s]
617.573584438 m 658.114651571 m
Image shape: (720, 1280, 3)
 44%|████▍     | 558/1261 [03:13<04:44,  2.47it/s]
1139.89653428 m 539.34692806 m
Image shape: (720, 1280, 3)
 44%|████▍     | 559/1261 [03:14<04:43,  2.47it/s]
438.219729412 m 266.136173956 m
Image shape: (720, 1280, 3)
 44%|████▍     | 560/1261 [03:14<04:40,  2.50it/s]
506.59373808 m 227.251088505 m
Image shape: (720, 1280, 3)
 44%|████▍     | 561/1261 [03:14<04:44,  2.46it/s]
1841.00578821 m 352.767144084 m
Image shape: (720, 1280, 3)
 45%|████▍     | 562/1261 [03:15<04:34,  2.54it/s]
484.804978396 m 383.853833328 m
Image shape: (720, 1280, 3)
 45%|████▍     | 563/1261 [03:15<05:18,  2.19it/s]
439.104244772 m 807.805841496 m
Image shape: (720, 1280, 3)
 45%|████▍     | 564/1261 [03:16<05:16,  2.20it/s]
202.148427306 m 287.890008434 m
Image shape: (720, 1280, 3)
 45%|████▍     | 565/1261 [03:16<05:01,  2.31it/s]
875.002547215 m 370.664215288 m
Image shape: (720, 1280, 3)
 45%|████▍     | 566/1261 [03:17<04:44,  2.44it/s]
81.1864029217 m 303.715271123 m
Image shape: (720, 1280, 3)
 45%|████▍     | 567/1261 [03:17<04:32,  2.54it/s]
309.837131643 m 335.757200201 m
Image shape: (720, 1280, 3)
 45%|████▌     | 568/1261 [03:17<04:29,  2.57it/s]
2490.51430482 m 1007.9591297 m
Image shape: (720, 1280, 3)
 45%|████▌     | 569/1261 [03:18<04:28,  2.57it/s]
478.948741342 m 560.976125386 m
Image shape: (720, 1280, 3)
 45%|████▌     | 570/1261 [03:18<04:23,  2.62it/s]
152.204985803 m 424.996843256 m
Image shape: (720, 1280, 3)
 45%|████▌     | 571/1261 [03:18<04:18,  2.67it/s]
910.761827771 m 561.108699839 m
Image shape: (720, 1280, 3)
 45%|████▌     | 572/1261 [03:19<04:14,  2.71it/s]
1160.41852117 m 1350.31349731 m
Image shape: (720, 1280, 3)
 45%|████▌     | 573/1261 [03:19<04:09,  2.76it/s]
1487.31675376 m 3852.21866652 m
Image shape: (720, 1280, 3)
 46%|████▌     | 574/1261 [03:20<04:12,  2.72it/s]
12293.3013604 m 859.032112389 m
Image shape: (720, 1280, 3)
 46%|████▌     | 575/1261 [03:20<04:12,  2.71it/s]
2149.87289888 m 660.597778971 m
Image shape: (720, 1280, 3)
 46%|████▌     | 576/1261 [03:20<04:10,  2.74it/s]
100.302151559 m 203.732459712 m
Image shape: (720, 1280, 3)
 46%|████▌     | 577/1261 [03:21<04:04,  2.80it/s]
1159.17847084 m 105.578569687 m
Image shape: (720, 1280, 3)
 46%|████▌     | 578/1261 [03:21<04:20,  2.63it/s]
55.2337657823 m 68.7218422405 m
Image shape: (720, 1280, 3)
 46%|████▌     | 579/1261 [03:21<04:27,  2.55it/s]
105.761171769 m 298.049399662 m
Image shape: (720, 1280, 3)
 46%|████▌     | 580/1261 [03:22<04:20,  2.61it/s]
65.1255970106 m 182.011646746 m
Image shape: (720, 1280, 3)
 46%|████▌     | 581/1261 [03:22<04:08,  2.74it/s]
1635.32525824 m 170.103107729 m
Image shape: (720, 1280, 3)
 46%|████▌     | 582/1261 [03:22<04:10,  2.71it/s]
256.170778467 m 336.231984686 m
Image shape: (720, 1280, 3)
 46%|████▌     | 583/1261 [03:23<04:03,  2.78it/s]
335.223822395 m 537.853431682 m
Image shape: (720, 1280, 3)
 46%|████▋     | 584/1261 [03:23<04:03,  2.78it/s]
90.2301523231 m 1074.83991378 m
Image shape: (720, 1280, 3)
 46%|████▋     | 585/1261 [03:24<04:00,  2.81it/s]
390.40193998 m 231.231782638 m
Image shape: (720, 1280, 3)
 46%|████▋     | 586/1261 [03:24<04:02,  2.79it/s]
9293.24525194 m 1065.65259846 m
Image shape: (720, 1280, 3)
 47%|████▋     | 587/1261 [03:24<04:04,  2.75it/s]
15.7503565116 m 465.096492943 m
Image shape: (720, 1280, 3)
 47%|████▋     | 588/1261 [03:25<03:55,  2.85it/s]
10.0877087995 m 319.6128733 m
Image shape: (720, 1280, 3)
 47%|████▋     | 589/1261 [03:25<03:56,  2.84it/s]
47.1080160553 m 247.272109504 m
Image shape: (720, 1280, 3)
 47%|████▋     | 590/1261 [03:25<03:56,  2.84it/s]
30.9384192874 m 354.415983822 m
Image shape: (720, 1280, 3)
 47%|████▋     | 591/1261 [03:26<03:56,  2.84it/s]
1802.02449394 m 512.222165866 m
Image shape: (720, 1280, 3)
 47%|████▋     | 592/1261 [03:26<03:47,  2.93it/s]
33.6773881349 m 5223.7796363 m
Image shape: (720, 1280, 3)
 47%|████▋     | 593/1261 [03:26<03:52,  2.87it/s]
37.0590927207 m 507.66292137 m
Image shape: (720, 1280, 3)
 47%|████▋     | 594/1261 [03:27<03:47,  2.93it/s]
53.886452611 m 222.641092138 m
Image shape: (720, 1280, 3)
 47%|████▋     | 595/1261 [03:27<03:53,  2.85it/s]
103.163061424 m 989.740449879 m
Image shape: (720, 1280, 3)
 47%|████▋     | 596/1261 [03:27<03:47,  2.93it/s]
39.6824198688 m 652.480060005 m
Image shape: (720, 1280, 3)
 47%|████▋     | 597/1261 [03:28<04:00,  2.76it/s]
1430.04747665 m 151.336639119 m
Image shape: (720, 1280, 3)
 47%|████▋     | 598/1261 [03:28<04:03,  2.73it/s]
106.68786855 m 98.9218169385 m
Image shape: (720, 1280, 3)
 48%|████▊     | 599/1261 [03:28<03:59,  2.76it/s]
57.7778116574 m 351.023098917 m
Image shape: (720, 1280, 3)
 48%|████▊     | 600/1261 [03:29<03:59,  2.75it/s]
90.9144238051 m 90.6249236688 m
Image shape: (720, 1280, 3)
 48%|████▊     | 601/1261 [03:29<04:01,  2.73it/s]
111.932163233 m 298.217800303 m
Image shape: (720, 1280, 3)
 48%|████▊     | 602/1261 [03:30<03:59,  2.75it/s]
1627.53514283 m 571.141884639 m
Image shape: (720, 1280, 3)
 48%|████▊     | 603/1261 [03:30<04:07,  2.66it/s]
187.282673033 m 347.721208988 m
Image shape: (720, 1280, 3)
 48%|████▊     | 604/1261 [03:30<04:08,  2.64it/s]
105.104862847 m 341.070823432 m
Image shape: (720, 1280, 3)
 48%|████▊     | 605/1261 [03:31<04:04,  2.68it/s]
16329.1187673 m 983.026456595 m
Image shape: (720, 1280, 3)
 48%|████▊     | 606/1261 [03:31<03:59,  2.73it/s]
189.124429907 m 1118.31553277 m
Image shape: (720, 1280, 3)
 48%|████▊     | 607/1261 [03:31<03:56,  2.77it/s]
233.261508964 m 1232.59269603 m
Image shape: (720, 1280, 3)
 48%|████▊     | 608/1261 [03:32<04:06,  2.65it/s]
138.133276213 m 1425.23488177 m
Image shape: (720, 1280, 3)
 48%|████▊     | 609/1261 [03:32<04:17,  2.53it/s]
195.959981674 m 2242.05212422 m
Image shape: (720, 1280, 3)
 48%|████▊     | 610/1261 [03:33<04:26,  2.45it/s]
1006.7334809 m 1185.53239652 m
Image shape: (720, 1280, 3)
 48%|████▊     | 611/1261 [03:33<04:29,  2.41it/s]
149.809132993 m 924.418443131 m
Image shape: (720, 1280, 3)
 49%|████▊     | 612/1261 [03:34<04:25,  2.44it/s]
1231.23553741 m 423.996949199 m
Image shape: (720, 1280, 3)
 49%|████▊     | 613/1261 [03:34<04:23,  2.46it/s]
3742.8175568 m 452.158746096 m
Image shape: (720, 1280, 3)
 49%|████▊     | 614/1261 [03:34<04:24,  2.44it/s]
158.234139291 m 459.495722316 m
Image shape: (720, 1280, 3)
 49%|████▉     | 615/1261 [03:35<04:46,  2.25it/s]
183.086965807 m 446.31151555 m
Image shape: (720, 1280, 3)
 49%|████▉     | 616/1261 [03:35<04:45,  2.26it/s]
160.097317498 m 666.321233617 m
Image shape: (720, 1280, 3)
 49%|████▉     | 617/1261 [03:36<04:33,  2.36it/s]
178.948511964 m 1214.39201682 m
Image shape: (720, 1280, 3)
 49%|████▉     | 618/1261 [03:36<04:24,  2.43it/s]
167.96997711 m 885.513038983 m
Image shape: (720, 1280, 3)
 49%|████▉     | 619/1261 [03:36<04:15,  2.51it/s]
80.6376382028 m 933.194742502 m
Image shape: (720, 1280, 3)
 49%|████▉     | 620/1261 [03:37<04:12,  2.54it/s]
76.5938258097 m 727.354316731 m
Image shape: (720, 1280, 3)
 49%|████▉     | 621/1261 [03:37<04:09,  2.57it/s]
1077.13633501 m 572.865550636 m
Image shape: (720, 1280, 3)
 49%|████▉     | 622/1261 [03:38<04:06,  2.59it/s]
207.615316695 m 765.97007229 m
Image shape: (720, 1280, 3)
 49%|████▉     | 623/1261 [03:38<04:11,  2.54it/s]
132.170203776 m 1166.9638172 m
Image shape: (720, 1280, 3)
 49%|████▉     | 624/1261 [03:38<04:05,  2.59it/s]
233.06149852 m 1200.49473073 m
Image shape: (720, 1280, 3)
 50%|████▉     | 625/1261 [03:39<04:06,  2.58it/s]
513.202071878 m 1352.48524602 m
Image shape: (720, 1280, 3)
 50%|████▉     | 626/1261 [03:39<04:35,  2.31it/s]
217.186511248 m 1342.26463836 m
Image shape: (720, 1280, 3)
 50%|████▉     | 627/1261 [03:40<04:34,  2.31it/s]
152.909381588 m 669.017296428 m
Image shape: (720, 1280, 3)
 50%|████▉     | 628/1261 [03:40<04:20,  2.43it/s]
108.806536544 m 788.097789771 m
Image shape: (720, 1280, 3)
 50%|████▉     | 629/1261 [03:41<04:22,  2.40it/s]
161.955025099 m 447.152303585 m
Image shape: (720, 1280, 3)
 50%|████▉     | 630/1261 [03:41<04:25,  2.37it/s]
134.159731819 m 318.982076999 m
Image shape: (720, 1280, 3)
 50%|█████     | 631/1261 [03:41<04:17,  2.44it/s]
147.010416384 m 365.231283913 m
Image shape: (720, 1280, 3)
 50%|█████     | 632/1261 [03:42<04:15,  2.47it/s]
94.4492327432 m 424.374481253 m
Image shape: (720, 1280, 3)
 50%|█████     | 633/1261 [03:42<04:11,  2.50it/s]
88.6087836296 m 551.292438695 m
Image shape: (720, 1280, 3)
 50%|█████     | 634/1261 [03:42<04:00,  2.61it/s]
304.864847407 m 385.383137512 m
Image shape: (720, 1280, 3)
 50%|█████     | 635/1261 [03:43<03:52,  2.69it/s]
273.147322683 m 474.677477771 m
Image shape: (720, 1280, 3)
 50%|█████     | 636/1261 [03:43<03:51,  2.70it/s]
255.949676316 m 829.659050398 m
Image shape: (720, 1280, 3)
 51%|█████     | 637/1261 [03:44<03:53,  2.67it/s]
197.157114905 m 758.565474116 m
Image shape: (720, 1280, 3)
 51%|█████     | 638/1261 [03:44<03:55,  2.65it/s]
280.725715063 m 634.027481897 m
Image shape: (720, 1280, 3)
 51%|█████     | 639/1261 [03:44<03:52,  2.68it/s]
306.585762756 m 690.179928295 m
Image shape: (720, 1280, 3)
 51%|█████     | 640/1261 [03:45<03:54,  2.65it/s]
349.299568839 m 505.266226732 m
Image shape: (720, 1280, 3)
 51%|█████     | 641/1261 [03:45<03:52,  2.66it/s]
248.457919126 m 394.895323945 m
Image shape: (720, 1280, 3)
 51%|█████     | 642/1261 [03:45<03:48,  2.70it/s]
335.209110099 m 328.005769939 m
Image shape: (720, 1280, 3)
 51%|█████     | 643/1261 [03:46<03:50,  2.68it/s]
297.751867511 m 410.607459626 m
Image shape: (720, 1280, 3)
 51%|█████     | 644/1261 [03:46<03:52,  2.65it/s]
293.963111473 m 475.761070875 m
Image shape: (720, 1280, 3)
 51%|█████     | 645/1261 [03:47<03:55,  2.62it/s]
231.211206722 m 572.267117665 m
Image shape: (720, 1280, 3)
 51%|█████     | 646/1261 [03:47<03:58,  2.58it/s]
450.076048282 m 519.276576951 m
Image shape: (720, 1280, 3)
 51%|█████▏    | 647/1261 [03:47<03:54,  2.62it/s]
739.262617015 m 735.890614559 m
Image shape: (720, 1280, 3)
 51%|█████▏    | 648/1261 [03:48<03:55,  2.61it/s]
7197.48151806 m 665.133585963 m
Image shape: (720, 1280, 3)
 51%|█████▏    | 649/1261 [03:48<03:50,  2.66it/s]
475.242547289 m 1055.91555115 m
Image shape: (720, 1280, 3)
 52%|█████▏    | 650/1261 [03:48<03:51,  2.64it/s]
443.879873749 m 865.8557229 m
Image shape: (720, 1280, 3)
 52%|█████▏    | 651/1261 [03:49<03:48,  2.67it/s]
612.37175731 m 699.846996788 m
Image shape: (720, 1280, 3)
 52%|█████▏    | 652/1261 [03:49<03:42,  2.73it/s]
424.338261438 m 776.70795381 m
Image shape: (720, 1280, 3)
 52%|█████▏    | 653/1261 [03:50<03:42,  2.73it/s]
491.372636121 m 494.061857071 m
Image shape: (720, 1280, 3)
 52%|█████▏    | 654/1261 [03:50<03:42,  2.73it/s]
877.217755409 m 432.995167869 m
Image shape: (720, 1280, 3)
 52%|█████▏    | 655/1261 [03:50<03:42,  2.72it/s]
757.220217767 m 447.424991019 m
Image shape: (720, 1280, 3)
 52%|█████▏    | 656/1261 [03:51<03:41,  2.73it/s]
544.181213784 m 292.509765489 m
Image shape: (720, 1280, 3)
 52%|█████▏    | 657/1261 [03:51<03:37,  2.78it/s]
858.331432886 m 299.522390542 m
Image shape: (720, 1280, 3)
 52%|█████▏    | 658/1261 [03:51<03:35,  2.80it/s]
1060.87956824 m 323.649593091 m
Image shape: (720, 1280, 3)
 52%|█████▏    | 659/1261 [03:52<03:40,  2.73it/s]
785.831842127 m 330.223951583 m
Image shape: (720, 1280, 3)
 52%|█████▏    | 660/1261 [03:52<03:43,  2.69it/s]
596.102464964 m 417.281001325 m
Image shape: (720, 1280, 3)
 52%|█████▏    | 661/1261 [03:53<03:43,  2.69it/s]
517.974380557 m 649.679173195 m
Image shape: (720, 1280, 3)
 52%|█████▏    | 662/1261 [03:53<03:40,  2.72it/s]
511.964087459 m 830.080793605 m
Image shape: (720, 1280, 3)
 53%|█████▎    | 663/1261 [03:53<03:41,  2.70it/s]
387.285447086 m 1099.96547859 m
Image shape: (720, 1280, 3)
 53%|█████▎    | 664/1261 [03:54<03:47,  2.63it/s]
438.894712145 m 1013.84755293 m
Image shape: (720, 1280, 3)
 53%|█████▎    | 665/1261 [03:54<03:49,  2.60it/s]
474.228150167 m 589.596322357 m
Image shape: (720, 1280, 3)
 53%|█████▎    | 666/1261 [03:54<03:49,  2.59it/s]
476.70222335 m 434.316613872 m
Image shape: (720, 1280, 3)
 53%|█████▎    | 667/1261 [03:55<03:48,  2.60it/s]
447.492893677 m 492.770855208 m
Image shape: (720, 1280, 3)
 53%|█████▎    | 668/1261 [03:55<03:40,  2.69it/s]
385.924322321 m 329.975948161 m
Image shape: (720, 1280, 3)
 53%|█████▎    | 669/1261 [03:56<03:39,  2.69it/s]
411.425157417 m 518.939421562 m
Image shape: (720, 1280, 3)
 53%|█████▎    | 670/1261 [03:56<03:37,  2.72it/s]
453.858776808 m 548.059411633 m
Image shape: (720, 1280, 3)
 53%|█████▎    | 671/1261 [03:56<03:39,  2.69it/s]
541.220861107 m 291.013401011 m
Image shape: (720, 1280, 3)
 53%|█████▎    | 672/1261 [03:57<03:39,  2.68it/s]
438.344408771 m 345.160407662 m
Image shape: (720, 1280, 3)
 53%|█████▎    | 673/1261 [03:57<03:40,  2.66it/s]
419.404948258 m 345.644644941 m
Image shape: (720, 1280, 3)
 53%|█████▎    | 674/1261 [03:57<03:39,  2.67it/s]
357.486147292 m 1031.1983022 m
Image shape: (720, 1280, 3)
 54%|█████▎    | 675/1261 [03:58<03:37,  2.69it/s]
381.4902615 m 744.395589358 m
Image shape: (720, 1280, 3)
 54%|█████▎    | 676/1261 [03:58<03:34,  2.72it/s]
430.205880162 m 838.59646063 m
Image shape: (720, 1280, 3)
 54%|█████▎    | 677/1261 [03:58<03:35,  2.71it/s]
358.74555912 m 596.471982267 m
Image shape: (720, 1280, 3)
 54%|█████▍    | 678/1261 [03:59<03:33,  2.73it/s]
399.611214922 m 578.948357366 m
Image shape: (720, 1280, 3)
 54%|█████▍    | 679/1261 [03:59<03:30,  2.76it/s]
465.537624487 m 342.745236572 m
Image shape: (720, 1280, 3)
 54%|█████▍    | 680/1261 [04:00<03:31,  2.75it/s]
583.005349775 m 361.776226307 m
Image shape: (720, 1280, 3)
 54%|█████▍    | 681/1261 [04:00<03:35,  2.69it/s]
423.176961734 m 366.302294778 m
Image shape: (720, 1280, 3)
 54%|█████▍    | 682/1261 [04:00<03:31,  2.74it/s]
424.167029164 m 383.84112214 m
Image shape: (720, 1280, 3)
 54%|█████▍    | 683/1261 [04:01<03:36,  2.67it/s]
554.350996664 m 2012.98349141 m
Image shape: (720, 1280, 3)
 54%|█████▍    | 684/1261 [04:01<03:34,  2.69it/s]
510.784802359 m 378.052942186 m
Image shape: (720, 1280, 3)
 54%|█████▍    | 685/1261 [04:01<03:31,  2.72it/s]
566.225904544 m 380.627627875 m
Image shape: (720, 1280, 3)
 54%|█████▍    | 686/1261 [04:02<03:29,  2.75it/s]
476.925580721 m 576.635723944 m
Image shape: (720, 1280, 3)
 54%|█████▍    | 687/1261 [04:02<03:28,  2.75it/s]
497.445597938 m 726.264161607 m
Image shape: (720, 1280, 3)
 55%|█████▍    | 688/1261 [04:03<03:25,  2.79it/s]
511.137670296 m 832.000950986 m
Image shape: (720, 1280, 3)
 55%|█████▍    | 689/1261 [04:03<03:31,  2.70it/s]
608.031553747 m 932.887486862 m
Image shape: (720, 1280, 3)
 55%|█████▍    | 690/1261 [04:03<03:35,  2.65it/s]
623.834716658 m 587.353807334 m
Image shape: (720, 1280, 3)
 55%|█████▍    | 691/1261 [04:04<03:41,  2.58it/s]
663.692587116 m 581.899600418 m
Image shape: (720, 1280, 3)
 55%|█████▍    | 692/1261 [04:04<03:42,  2.56it/s]
622.198704168 m 590.119140249 m
Image shape: (720, 1280, 3)
 55%|█████▍    | 693/1261 [04:04<03:34,  2.65it/s]
599.737666167 m 691.206308476 m
Image shape: (720, 1280, 3)
 55%|█████▌    | 694/1261 [04:05<03:31,  2.68it/s]
675.155119238 m 870.937802278 m
Image shape: (720, 1280, 3)
 55%|█████▌    | 695/1261 [04:05<03:56,  2.40it/s]
765.43640377 m 1229.33299863 m
Image shape: (720, 1280, 3)
 55%|█████▌    | 696/1261 [04:06<04:03,  2.32it/s]
635.062385644 m 1882.70932937 m
Image shape: (720, 1280, 3)
 55%|█████▌    | 697/1261 [04:06<04:00,  2.34it/s]
564.878713561 m 1470.56624839 m
Image shape: (720, 1280, 3)
 55%|█████▌    | 698/1261 [04:07<03:57,  2.37it/s]
701.201393131 m 1220.12060724 m
Image shape: (720, 1280, 3)
 55%|█████▌    | 699/1261 [04:07<03:53,  2.40it/s]
580.956972614 m 920.545776546 m
Image shape: (720, 1280, 3)
 56%|█████▌    | 700/1261 [04:07<03:52,  2.41it/s]
406.700490373 m 1022.23782493 m
Image shape: (720, 1280, 3)
 56%|█████▌    | 701/1261 [04:08<03:51,  2.42it/s]
387.925086814 m 748.103865593 m
Image shape: (720, 1280, 3)
 56%|█████▌    | 702/1261 [04:08<03:39,  2.55it/s]
406.226373159 m 755.039541813 m
Image shape: (720, 1280, 3)
 56%|█████▌    | 703/1261 [04:09<03:32,  2.62it/s]
333.95730376 m 754.747377819 m
Image shape: (720, 1280, 3)
 56%|█████▌    | 704/1261 [04:09<03:42,  2.50it/s]
394.311154186 m 601.777574778 m
Image shape: (720, 1280, 3)
 56%|█████▌    | 705/1261 [04:09<03:41,  2.51it/s]
396.544792473 m 375.384044108 m
Image shape: (720, 1280, 3)
 56%|█████▌    | 706/1261 [04:10<03:33,  2.60it/s]
348.173542109 m 419.976912279 m
Image shape: (720, 1280, 3)
 56%|█████▌    | 707/1261 [04:10<03:27,  2.66it/s]
441.057061018 m 459.731789108 m
Image shape: (720, 1280, 3)
 56%|█████▌    | 708/1261 [04:11<03:49,  2.41it/s]
498.995848244 m 437.571297086 m
Image shape: (720, 1280, 3)
 56%|█████▌    | 709/1261 [04:11<03:44,  2.45it/s]
498.329911336 m 588.868058967 m
Image shape: (720, 1280, 3)
 56%|█████▋    | 710/1261 [04:11<03:49,  2.40it/s]
492.425124336 m 660.401279796 m
Image shape: (720, 1280, 3)
 56%|█████▋    | 711/1261 [04:12<04:00,  2.28it/s]
506.486247279 m 843.179803994 m
Image shape: (720, 1280, 3)
 56%|█████▋    | 712/1261 [04:12<03:43,  2.46it/s]
531.429795781 m 922.946183537 m
Image shape: (720, 1280, 3)
 57%|█████▋    | 713/1261 [04:13<03:35,  2.54it/s]
571.426167764 m 998.564856658 m
Image shape: (720, 1280, 3)
 57%|█████▋    | 714/1261 [04:13<03:26,  2.65it/s]
383.194151771 m 649.735515328 m
Image shape: (720, 1280, 3)
 57%|█████▋    | 715/1261 [04:13<03:19,  2.74it/s]
460.999865325 m 680.636210993 m
Image shape: (720, 1280, 3)
 57%|█████▋    | 716/1261 [04:14<03:17,  2.76it/s]
399.948094431 m 804.519433444 m
Image shape: (720, 1280, 3)
 57%|█████▋    | 717/1261 [04:14<03:15,  2.79it/s]
468.534229112 m 818.135103585 m
Image shape: (720, 1280, 3)
 57%|█████▋    | 718/1261 [04:14<03:18,  2.73it/s]
391.480592765 m 1104.04186895 m
Image shape: (720, 1280, 3)
 57%|█████▋    | 719/1261 [04:15<03:27,  2.62it/s]
430.172537381 m 1280.03909442 m
Image shape: (720, 1280, 3)
 57%|█████▋    | 720/1261 [04:15<03:18,  2.72it/s]
474.969076851 m 1062.26235884 m
Image shape: (720, 1280, 3)
 57%|█████▋    | 721/1261 [04:15<03:15,  2.76it/s]
452.717611083 m 519.017210332 m
Image shape: (720, 1280, 3)
 57%|█████▋    | 722/1261 [04:16<03:11,  2.82it/s]
468.9832341 m 596.04530618 m
Image shape: (720, 1280, 3)
 57%|█████▋    | 723/1261 [04:16<03:06,  2.89it/s]
477.378977091 m 693.281053261 m
Image shape: (720, 1280, 3)
 57%|█████▋    | 724/1261 [04:16<03:07,  2.87it/s]
430.759341621 m 1044.52366804 m
Image shape: (720, 1280, 3)
 57%|█████▋    | 725/1261 [04:17<03:05,  2.89it/s]
415.191211679 m 1018.16107873 m
Image shape: (720, 1280, 3)
 58%|█████▊    | 726/1261 [04:17<03:03,  2.91it/s]
411.037641495 m 1131.24047155 m
Image shape: (720, 1280, 3)
 58%|█████▊    | 727/1261 [04:18<03:01,  2.94it/s]
422.883887172 m 676.570949095 m
Image shape: (720, 1280, 3)
 58%|█████▊    | 728/1261 [04:18<03:03,  2.91it/s]
415.672894472 m 547.018065665 m
Image shape: (720, 1280, 3)
 58%|█████▊    | 729/1261 [04:18<03:01,  2.93it/s]
397.154730914 m 384.394355659 m
Image shape: (720, 1280, 3)
 58%|█████▊    | 730/1261 [04:19<03:01,  2.93it/s]
429.727737497 m 409.236302398 m
Image shape: (720, 1280, 3)
 58%|█████▊    | 731/1261 [04:19<03:00,  2.93it/s]
416.77919559 m 710.051810545 m
Image shape: (720, 1280, 3)
 58%|█████▊    | 732/1261 [04:19<03:04,  2.87it/s]
436.296752502 m 918.199760655 m
Image shape: (720, 1280, 3)
 58%|█████▊    | 733/1261 [04:20<03:04,  2.87it/s]
446.716520101 m 478.420618471 m
Image shape: (720, 1280, 3)
 58%|█████▊    | 734/1261 [04:20<03:04,  2.85it/s]
348.412163565 m 695.240603268 m
Image shape: (720, 1280, 3)
 58%|█████▊    | 735/1261 [04:20<03:02,  2.89it/s]
366.175157636 m 918.943722342 m
Image shape: (720, 1280, 3)
 58%|█████▊    | 736/1261 [04:21<02:59,  2.93it/s]
322.182625082 m 1383.25950375 m
Image shape: (720, 1280, 3)
 58%|█████▊    | 737/1261 [04:21<02:59,  2.92it/s]
395.775092808 m 879.659637703 m
Image shape: (720, 1280, 3)
 59%|█████▊    | 738/1261 [04:21<02:59,  2.91it/s]
253.522975178 m 937.203047083 m
Image shape: (720, 1280, 3)
 59%|█████▊    | 739/1261 [04:22<02:57,  2.94it/s]
312.627904872 m 582.861213104 m
Image shape: (720, 1280, 3)
 59%|█████▊    | 740/1261 [04:22<02:55,  2.97it/s]
291.981357676 m 308.577514508 m
Image shape: (720, 1280, 3)
 59%|█████▉    | 741/1261 [04:22<02:54,  2.99it/s]
270.549730983 m 469.966625571 m
Image shape: (720, 1280, 3)
 59%|█████▉    | 742/1261 [04:23<02:51,  3.03it/s]
286.963194581 m 333.362004618 m
Image shape: (720, 1280, 3)
 59%|█████▉    | 743/1261 [04:23<02:50,  3.04it/s]
287.748022938 m 522.683802701 m
Image shape: (720, 1280, 3)
 59%|█████▉    | 744/1261 [04:23<02:48,  3.06it/s]
369.246258193 m 612.207215445 m
Image shape: (720, 1280, 3)
 59%|█████▉    | 745/1261 [04:24<02:49,  3.05it/s]
530.019125424 m 382.684903993 m
Image shape: (720, 1280, 3)
 59%|█████▉    | 746/1261 [04:24<02:49,  3.04it/s]
380.163821097 m 539.681901049 m
Image shape: (720, 1280, 3)
 59%|█████▉    | 747/1261 [04:24<02:48,  3.04it/s]
389.759501251 m 816.978147389 m
Image shape: (720, 1280, 3)
 59%|█████▉    | 748/1261 [04:25<02:49,  3.03it/s]
327.189090673 m 700.756308133 m
Image shape: (720, 1280, 3)
 59%|█████▉    | 749/1261 [04:25<02:48,  3.03it/s]
327.732567125 m 866.012733677 m
Image shape: (720, 1280, 3)
 59%|█████▉    | 750/1261 [04:25<02:47,  3.05it/s]
378.283128432 m 932.457364704 m
Image shape: (720, 1280, 3)
 60%|█████▉    | 751/1261 [04:26<02:46,  3.06it/s]
351.46065764 m 395.251928412 m
Image shape: (720, 1280, 3)
 60%|█████▉    | 752/1261 [04:26<02:45,  3.07it/s]
387.814132865 m 300.451156571 m
Image shape: (720, 1280, 3)
 60%|█████▉    | 753/1261 [04:26<02:46,  3.06it/s]
394.434116287 m 317.090178926 m
Image shape: (720, 1280, 3)
 60%|█████▉    | 754/1261 [04:27<02:45,  3.06it/s]
395.805802318 m 368.063328329 m
Image shape: (720, 1280, 3)
 60%|█████▉    | 755/1261 [04:27<02:44,  3.08it/s]
405.112539551 m 353.247703112 m
Image shape: (720, 1280, 3)
 60%|█████▉    | 756/1261 [04:27<02:44,  3.06it/s]
382.442676019 m 349.458950875 m
Image shape: (720, 1280, 3)
 60%|██████    | 757/1261 [04:28<02:44,  3.06it/s]
440.414560628 m 432.408114841 m
Image shape: (720, 1280, 3)
 60%|██████    | 758/1261 [04:28<02:44,  3.06it/s]
441.304777062 m 458.749963381 m
Image shape: (720, 1280, 3)
 60%|██████    | 759/1261 [04:28<02:44,  3.06it/s]
449.989917035 m 627.147156302 m
Image shape: (720, 1280, 3)
 60%|██████    | 760/1261 [04:29<02:44,  3.05it/s]
385.189841226 m 809.987320728 m
Image shape: (720, 1280, 3)
 60%|██████    | 761/1261 [04:29<02:44,  3.04it/s]
419.457369686 m 961.972479249 m
Image shape: (720, 1280, 3)
 60%|██████    | 762/1261 [04:29<02:44,  3.03it/s]
434.48691784 m 1024.20044208 m
Image shape: (720, 1280, 3)
 61%|██████    | 763/1261 [04:30<02:45,  3.02it/s]
488.417884284 m 410.670361103 m
Image shape: (720, 1280, 3)
 61%|██████    | 764/1261 [04:30<02:44,  3.02it/s]
504.344562737 m 308.233049463 m
Image shape: (720, 1280, 3)
 61%|██████    | 765/1261 [04:30<02:43,  3.04it/s]
460.934197392 m 349.850149356 m
Image shape: (720, 1280, 3)
 61%|██████    | 766/1261 [04:30<02:43,  3.04it/s]
413.679451466 m 359.434362834 m
Image shape: (720, 1280, 3)
 61%|██████    | 767/1261 [04:31<02:42,  3.04it/s]
432.08862069 m 415.892499794 m
Image shape: (720, 1280, 3)
 61%|██████    | 768/1261 [04:31<02:41,  3.06it/s]
395.602797545 m 433.081769416 m
Image shape: (720, 1280, 3)
 61%|██████    | 769/1261 [04:31<02:42,  3.03it/s]
408.743872854 m 417.282953078 m
Image shape: (720, 1280, 3)
 61%|██████    | 770/1261 [04:32<02:41,  3.04it/s]
354.162135951 m 664.871050175 m
Image shape: (720, 1280, 3)
 61%|██████    | 771/1261 [04:32<02:40,  3.06it/s]
361.562317303 m 858.429831914 m
Image shape: (720, 1280, 3)
 61%|██████    | 772/1261 [04:32<02:39,  3.06it/s]
359.093357528 m 838.661012075 m
Image shape: (720, 1280, 3)
 61%|██████▏   | 773/1261 [04:33<02:38,  3.07it/s]
351.031949137 m 1020.64814169 m
Image shape: (720, 1280, 3)
 61%|██████▏   | 774/1261 [04:33<02:40,  3.03it/s]
351.640679934 m 607.422358456 m
Image shape: (720, 1280, 3)
 61%|██████▏   | 775/1261 [04:33<02:43,  2.96it/s]
358.540219005 m 438.273906716 m
Image shape: (720, 1280, 3)
 62%|██████▏   | 776/1261 [04:34<02:42,  2.99it/s]
434.255295918 m 335.575312995 m
Image shape: (720, 1280, 3)
 62%|██████▏   | 777/1261 [04:34<02:41,  3.00it/s]
447.953164131 m 315.259759915 m
Image shape: (720, 1280, 3)
 62%|██████▏   | 778/1261 [04:34<02:40,  3.02it/s]
378.721373316 m 597.188762735 m
Image shape: (720, 1280, 3)
 62%|██████▏   | 779/1261 [04:35<02:39,  3.02it/s]
384.233713187 m 626.076205467 m
Image shape: (720, 1280, 3)
 62%|██████▏   | 780/1261 [04:35<02:38,  3.03it/s]
478.735236656 m 608.947930892 m
Image shape: (720, 1280, 3)
 62%|██████▏   | 781/1261 [04:35<02:38,  3.03it/s]
439.890581215 m 447.286815383 m
Image shape: (720, 1280, 3)
 62%|██████▏   | 782/1261 [04:36<02:38,  3.02it/s]
458.549608285 m 499.081900951 m
Image shape: (720, 1280, 3)
 62%|██████▏   | 783/1261 [04:36<02:39,  3.00it/s]
453.858757457 m 585.371316191 m
Image shape: (720, 1280, 3)
 62%|██████▏   | 784/1261 [04:36<02:39,  2.99it/s]
516.545007085 m 697.300132567 m
Image shape: (720, 1280, 3)
 62%|██████▏   | 785/1261 [04:37<02:38,  3.01it/s]
480.224441816 m 754.76902559 m
Image shape: (720, 1280, 3)
 62%|██████▏   | 786/1261 [04:37<02:36,  3.03it/s]
438.034944879 m 744.677870217 m
Image shape: (720, 1280, 3)
 62%|██████▏   | 787/1261 [04:37<02:35,  3.04it/s]
454.46923441 m 549.248359363 m
Image shape: (720, 1280, 3)
 62%|██████▏   | 788/1261 [04:38<02:36,  3.03it/s]
496.166081707 m 480.514773948 m
Image shape: (720, 1280, 3)
 63%|██████▎   | 789/1261 [04:38<02:37,  3.00it/s]
484.328580381 m 516.543891721 m
Image shape: (720, 1280, 3)
 63%|██████▎   | 790/1261 [04:38<02:38,  2.98it/s]
445.686053031 m 509.493002534 m
Image shape: (720, 1280, 3)
 63%|██████▎   | 791/1261 [04:39<02:36,  3.01it/s]
624.857210575 m 601.150688359 m
Image shape: (720, 1280, 3)
 63%|██████▎   | 792/1261 [04:39<02:34,  3.03it/s]
541.355960877 m 346.968196513 m
Image shape: (720, 1280, 3)
 63%|██████▎   | 793/1261 [04:39<02:35,  3.02it/s]
439.029521784 m 366.974151177 m
Image shape: (720, 1280, 3)
 63%|██████▎   | 794/1261 [04:40<02:34,  3.02it/s]
450.410631296 m 556.374152908 m
Image shape: (720, 1280, 3)
 63%|██████▎   | 795/1261 [04:40<02:34,  3.02it/s]
417.828313661 m 715.418539851 m
Image shape: (720, 1280, 3)
 63%|██████▎   | 796/1261 [04:40<02:33,  3.02it/s]
403.578871136 m 896.241687448 m
Image shape: (720, 1280, 3)
 63%|██████▎   | 797/1261 [04:41<02:33,  3.03it/s]
472.427588496 m 1052.35498169 m
Image shape: (720, 1280, 3)
 63%|██████▎   | 798/1261 [04:41<02:33,  3.02it/s]
467.642722847 m 865.540898367 m
Image shape: (720, 1280, 3)
 63%|██████▎   | 799/1261 [04:41<02:34,  2.99it/s]
547.321924581 m 771.207155863 m
Image shape: (720, 1280, 3)
 63%|██████▎   | 800/1261 [04:42<02:33,  3.00it/s]
726.134132611 m 683.629825249 m
Image shape: (720, 1280, 3)
 64%|██████▎   | 801/1261 [04:42<02:32,  3.02it/s]
923.768821102 m 693.009938236 m
Image shape: (720, 1280, 3)
 64%|██████▎   | 802/1261 [04:42<02:31,  3.03it/s]
798.301567791 m 444.4195497 m
Image shape: (720, 1280, 3)
 64%|██████▎   | 803/1261 [04:43<02:31,  3.03it/s]
1112.13422758 m 489.425624645 m
Image shape: (720, 1280, 3)
 64%|██████▍   | 804/1261 [04:43<02:30,  3.04it/s]
1147.09169266 m 446.576549589 m
Image shape: (720, 1280, 3)
 64%|██████▍   | 805/1261 [04:43<02:29,  3.05it/s]
792.070958963 m 809.531345319 m
Image shape: (720, 1280, 3)
 64%|██████▍   | 806/1261 [04:44<02:30,  3.03it/s]
645.636828473 m 1035.62758321 m
Image shape: (720, 1280, 3)
 64%|██████▍   | 807/1261 [04:44<02:29,  3.04it/s]
560.88735525 m 1308.80670609 m
Image shape: (720, 1280, 3)
 64%|██████▍   | 808/1261 [04:44<02:28,  3.04it/s]
583.405995695 m 1569.18545196 m
Image shape: (720, 1280, 3)
 64%|██████▍   | 809/1261 [04:45<02:28,  3.05it/s]
654.9066961 m 1301.39002261 m
Image shape: (720, 1280, 3)
 64%|██████▍   | 810/1261 [04:45<02:27,  3.05it/s]
732.219010088 m 1202.67980812 m
Image shape: (720, 1280, 3)
 64%|██████▍   | 811/1261 [04:45<02:26,  3.07it/s]
610.958771737 m 1085.94029547 m
Image shape: (720, 1280, 3)
 64%|██████▍   | 812/1261 [04:46<02:26,  3.07it/s]
679.755560706 m 818.673646037 m
Image shape: (720, 1280, 3)
 64%|██████▍   | 813/1261 [04:46<02:26,  3.05it/s]
702.299907858 m 865.928773563 m
Image shape: (720, 1280, 3)
 65%|██████▍   | 814/1261 [04:46<02:25,  3.07it/s]
730.708959784 m 1042.19824451 m
Image shape: (720, 1280, 3)
 65%|██████▍   | 815/1261 [04:47<02:25,  3.08it/s]
642.40153739 m 907.234895037 m
Image shape: (720, 1280, 3)
 65%|██████▍   | 816/1261 [04:47<02:25,  3.06it/s]
607.488035206 m 390.816515095 m
Image shape: (720, 1280, 3)
 65%|██████▍   | 817/1261 [04:47<02:24,  3.07it/s]
502.950305039 m 476.887422204 m
Image shape: (720, 1280, 3)
 65%|██████▍   | 818/1261 [04:48<02:25,  3.06it/s]
463.833082909 m 550.986046866 m
Image shape: (720, 1280, 3)
 65%|██████▍   | 819/1261 [04:48<02:24,  3.05it/s]
445.623314149 m 577.835063252 m
Image shape: (720, 1280, 3)
 65%|██████▌   | 820/1261 [04:48<02:24,  3.05it/s]
482.846845585 m 595.403645258 m
Image shape: (720, 1280, 3)
 65%|██████▌   | 821/1261 [04:49<02:23,  3.06it/s]
534.356177787 m 602.82247642 m
Image shape: (720, 1280, 3)
 65%|██████▌   | 822/1261 [04:49<02:23,  3.05it/s]
532.701109392 m 596.549863178 m
Image shape: (720, 1280, 3)
 65%|██████▌   | 823/1261 [04:49<02:23,  3.04it/s]
568.865816287 m 539.142650784 m
Image shape: (720, 1280, 3)
 65%|██████▌   | 824/1261 [04:50<02:23,  3.05it/s]
620.543472092 m 558.960626896 m
Image shape: (720, 1280, 3)
 65%|██████▌   | 825/1261 [04:50<02:22,  3.05it/s]
634.07644634 m 659.305526473 m
Image shape: (720, 1280, 3)
 66%|██████▌   | 826/1261 [04:50<02:22,  3.05it/s]
595.965820174 m 736.093789401 m
Image shape: (720, 1280, 3)
 66%|██████▌   | 827/1261 [04:51<02:22,  3.05it/s]
696.497560122 m 861.30716077 m
Image shape: (720, 1280, 3)
 66%|██████▌   | 828/1261 [04:51<02:22,  3.04it/s]
925.211964099 m 346.195497048 m
Image shape: (720, 1280, 3)
 66%|██████▌   | 829/1261 [04:51<02:22,  3.04it/s]
612.259917954 m 360.682443733 m
Image shape: (720, 1280, 3)
 66%|██████▌   | 830/1261 [04:52<02:21,  3.05it/s]
720.862761621 m 472.112575079 m
Image shape: (720, 1280, 3)
 66%|██████▌   | 831/1261 [04:52<02:20,  3.07it/s]
685.187422356 m 631.742330987 m
Image shape: (720, 1280, 3)
 66%|██████▌   | 832/1261 [04:52<02:21,  3.04it/s]
806.913915923 m 629.208749332 m
Image shape: (720, 1280, 3)
 66%|██████▌   | 833/1261 [04:53<02:20,  3.04it/s]
881.806475737 m 539.77753487 m
Image shape: (720, 1280, 3)
 66%|██████▌   | 834/1261 [04:53<02:20,  3.05it/s]
582.183351116 m 540.597891586 m
Image shape: (720, 1280, 3)
 66%|██████▌   | 835/1261 [04:53<02:19,  3.05it/s]
653.113315025 m 473.554784513 m
Image shape: (720, 1280, 3)
 66%|██████▋   | 836/1261 [04:54<02:20,  3.03it/s]
740.030289002 m 497.821330141 m
Image shape: (720, 1280, 3)
 66%|██████▋   | 837/1261 [04:54<02:19,  3.04it/s]
627.967118357 m 546.458015099 m
Image shape: (720, 1280, 3)
 66%|██████▋   | 838/1261 [04:54<02:18,  3.05it/s]
576.521977881 m 422.961358031 m
Image shape: (720, 1280, 3)
 67%|██████▋   | 839/1261 [04:55<02:17,  3.06it/s]
693.351072577 m 750.466919062 m
Image shape: (720, 1280, 3)
 67%|██████▋   | 840/1261 [04:55<02:17,  3.07it/s]
699.038409507 m 352.607953948 m
Image shape: (720, 1280, 3)
 67%|██████▋   | 841/1261 [04:55<02:17,  3.06it/s]
652.664765612 m 869.159059097 m
Image shape: (720, 1280, 3)
 67%|██████▋   | 842/1261 [04:56<02:16,  3.06it/s]
599.168402748 m 912.803882743 m
Image shape: (720, 1280, 3)
 67%|██████▋   | 843/1261 [04:56<02:16,  3.07it/s]
552.653835219 m 550.320782535 m
Image shape: (720, 1280, 3)
 67%|██████▋   | 844/1261 [04:56<02:17,  3.04it/s]
585.501730738 m 638.994301955 m
Image shape: (720, 1280, 3)
 67%|██████▋   | 845/1261 [04:56<02:15,  3.07it/s]
568.910310223 m 692.657865663 m
Image shape: (720, 1280, 3)
 67%|██████▋   | 846/1261 [04:57<02:15,  3.07it/s]
561.67902227 m 486.154439654 m
Image shape: (720, 1280, 3)
 67%|██████▋   | 847/1261 [04:57<02:14,  3.07it/s]
491.554497475 m 416.801013597 m
Image shape: (720, 1280, 3)
 67%|██████▋   | 848/1261 [04:57<02:14,  3.07it/s]
468.793724831 m 407.624045789 m
Image shape: (720, 1280, 3)
 67%|██████▋   | 849/1261 [04:58<02:13,  3.08it/s]
453.840393032 m 437.844988346 m
Image shape: (720, 1280, 3)
 67%|██████▋   | 850/1261 [04:58<02:13,  3.08it/s]
468.539297813 m 467.642896079 m
Image shape: (720, 1280, 3)
 67%|██████▋   | 851/1261 [04:58<02:14,  3.05it/s]
538.890499139 m 418.553306226 m
Image shape: (720, 1280, 3)
 68%|██████▊   | 852/1261 [04:59<02:15,  3.02it/s]
524.768608446 m 436.655972284 m
Image shape: (720, 1280, 3)
 68%|██████▊   | 853/1261 [04:59<02:15,  3.00it/s]
476.353243154 m 540.241656008 m
Image shape: (720, 1280, 3)
 68%|██████▊   | 854/1261 [04:59<02:14,  3.02it/s]
431.123213456 m 671.276347913 m
Image shape: (720, 1280, 3)
 68%|██████▊   | 855/1261 [05:00<02:14,  3.03it/s]
441.856486914 m 816.240743546 m
Image shape: (720, 1280, 3)
 68%|██████▊   | 856/1261 [05:00<02:13,  3.04it/s]
554.167137986 m 775.151002284 m
Image shape: (720, 1280, 3)
 68%|██████▊   | 857/1261 [05:00<02:12,  3.05it/s]
541.94816496 m 513.332639496 m
Image shape: (720, 1280, 3)
 68%|██████▊   | 858/1261 [05:01<02:11,  3.06it/s]
465.711281859 m 420.865199801 m
Image shape: (720, 1280, 3)
 68%|██████▊   | 859/1261 [05:01<02:11,  3.05it/s]
610.667639355 m 293.418607986 m
Image shape: (720, 1280, 3)
 68%|██████▊   | 860/1261 [05:01<02:12,  3.03it/s]
647.664482247 m 409.592722241 m
Image shape: (720, 1280, 3)
 68%|██████▊   | 861/1261 [05:02<02:11,  3.03it/s]
637.733299406 m 522.675621865 m
Image shape: (720, 1280, 3)
 68%|██████▊   | 862/1261 [05:02<02:11,  3.03it/s]
692.298462411 m 745.429813702 m
Image shape: (720, 1280, 3)
 68%|██████▊   | 863/1261 [05:02<02:11,  3.02it/s]
693.238606436 m 375.257082177 m
Image shape: (720, 1280, 3)
 69%|██████▊   | 864/1261 [05:03<02:11,  3.02it/s]
574.400579934 m 386.828462181 m
Image shape: (720, 1280, 3)
 69%|██████▊   | 865/1261 [05:03<02:10,  3.04it/s]
584.081995907 m 487.365281318 m
Image shape: (720, 1280, 3)
 69%|██████▊   | 866/1261 [05:03<02:09,  3.05it/s]
509.231297411 m 569.756957854 m
Image shape: (720, 1280, 3)
 69%|██████▉   | 867/1261 [05:04<02:11,  2.99it/s]
573.004733825 m 529.140448221 m
Image shape: (720, 1280, 3)
 69%|██████▉   | 868/1261 [05:04<02:11,  2.98it/s]
544.916863235 m 839.340376433 m
Image shape: (720, 1280, 3)
 69%|██████▉   | 869/1261 [05:04<02:10,  3.01it/s]
632.188367819 m 740.625204957 m
Image shape: (720, 1280, 3)
 69%|██████▉   | 870/1261 [05:05<02:08,  3.04it/s]
664.5299213 m 364.826567228 m
Image shape: (720, 1280, 3)
 69%|██████▉   | 871/1261 [05:05<02:08,  3.03it/s]
839.311592196 m 357.38810058 m
Image shape: (720, 1280, 3)
 69%|██████▉   | 872/1261 [05:05<02:07,  3.05it/s]
723.681066394 m 382.424096428 m
Image shape: (720, 1280, 3)
 69%|██████▉   | 873/1261 [05:06<02:07,  3.05it/s]
854.0026937 m 436.806057314 m
Image shape: (720, 1280, 3)
 69%|██████▉   | 874/1261 [05:06<02:06,  3.07it/s]
929.684547406 m 478.586298821 m
Image shape: (720, 1280, 3)
 69%|██████▉   | 875/1261 [05:06<02:06,  3.06it/s]
981.574253145 m 452.650359968 m
Image shape: (720, 1280, 3)
 69%|██████▉   | 876/1261 [05:07<02:05,  3.06it/s]
734.417776429 m 485.362436517 m
Image shape: (720, 1280, 3)
 70%|██████▉   | 877/1261 [05:07<02:05,  3.06it/s]
657.49343176 m 656.46492031 m
Image shape: (720, 1280, 3)
 70%|██████▉   | 878/1261 [05:07<02:05,  3.06it/s]
576.925763143 m 751.291074681 m
Image shape: (720, 1280, 3)
 70%|██████▉   | 879/1261 [05:08<02:05,  3.05it/s]
618.611474501 m 1222.67177684 m
Image shape: (720, 1280, 3)
 70%|██████▉   | 880/1261 [05:08<02:04,  3.05it/s]
565.484712993 m 1068.85579684 m
Image shape: (720, 1280, 3)
 70%|██████▉   | 881/1261 [05:08<02:04,  3.05it/s]
623.380960545 m 388.432793128 m
Image shape: (720, 1280, 3)
 70%|██████▉   | 882/1261 [05:09<02:03,  3.07it/s]
573.318628366 m 377.718509289 m
Image shape: (720, 1280, 3)
 70%|███████   | 883/1261 [05:09<02:03,  3.07it/s]
576.243286449 m 394.426110927 m
Image shape: (720, 1280, 3)
 70%|███████   | 884/1261 [05:09<02:02,  3.07it/s]
622.889693553 m 454.105046414 m
Image shape: (720, 1280, 3)
 70%|███████   | 885/1261 [05:10<02:03,  3.05it/s]
554.40371329 m 559.605368464 m
Image shape: (720, 1280, 3)
 70%|███████   | 886/1261 [05:10<02:03,  3.05it/s]
528.899633214 m 423.208025666 m
Image shape: (720, 1280, 3)
 70%|███████   | 887/1261 [05:10<02:03,  3.03it/s]
491.177284112 m 450.097770177 m
Image shape: (720, 1280, 3)
 70%|███████   | 888/1261 [05:11<02:02,  3.04it/s]
446.271195275 m 411.256083913 m
Image shape: (720, 1280, 3)
 70%|███████   | 889/1261 [05:11<02:02,  3.05it/s]
508.605730648 m 651.639112673 m
Image shape: (720, 1280, 3)
 71%|███████   | 890/1261 [05:12<02:45,  2.24it/s]
459.13174099 m 774.165121263 m
Image shape: (720, 1280, 3)
 71%|███████   | 891/1261 [05:12<02:32,  2.43it/s]
503.052381501 m 607.535917982 m
Image shape: (720, 1280, 3)
 71%|███████   | 892/1261 [05:12<02:23,  2.57it/s]
608.294727722 m 358.501122067 m
Image shape: (720, 1280, 3)
 71%|███████   | 893/1261 [05:13<02:15,  2.71it/s]
939.166622355 m 531.903097076 m
Image shape: (720, 1280, 3)
 71%|███████   | 894/1261 [05:13<02:10,  2.82it/s]
659.023528514 m 396.315610959 m
Image shape: (720, 1280, 3)
 71%|███████   | 895/1261 [05:13<02:06,  2.88it/s]
670.528511195 m 431.26527878 m
Image shape: (720, 1280, 3)
 71%|███████   | 896/1261 [05:14<02:05,  2.91it/s]
658.755635961 m 426.119679351 m
Image shape: (720, 1280, 3)
 71%|███████   | 897/1261 [05:14<02:03,  2.94it/s]
645.175364926 m 498.162792025 m
Image shape: (720, 1280, 3)
 71%|███████   | 898/1261 [05:14<02:02,  2.97it/s]
751.024477423 m 438.996845923 m
Image shape: (720, 1280, 3)
 71%|███████▏  | 899/1261 [05:15<02:01,  2.99it/s]
846.249836313 m 535.190591785 m
Image shape: (720, 1280, 3)
 71%|███████▏  | 900/1261 [05:15<02:00,  2.99it/s]
834.374067008 m 635.822287646 m
Image shape: (720, 1280, 3)
 71%|███████▏  | 901/1261 [05:15<01:59,  3.01it/s]
584.226685593 m 625.845571555 m
Image shape: (720, 1280, 3)
 72%|███████▏  | 902/1261 [05:16<01:59,  3.01it/s]
646.255889097 m 671.749428469 m
Image shape: (720, 1280, 3)
 72%|███████▏  | 903/1261 [05:16<01:58,  3.02it/s]
629.996616117 m 1645.59755411 m
Image shape: (720, 1280, 3)
 72%|███████▏  | 904/1261 [05:16<01:57,  3.04it/s]
500.51205267 m 492.644451356 m
Image shape: (720, 1280, 3)
 72%|███████▏  | 905/1261 [05:17<01:56,  3.05it/s]
603.304270484 m 510.799793566 m
Image shape: (720, 1280, 3)
 72%|███████▏  | 906/1261 [05:17<01:56,  3.05it/s]
612.15303961 m 383.256900831 m
Image shape: (720, 1280, 3)
 72%|███████▏  | 907/1261 [05:17<01:55,  3.07it/s]
549.160978571 m 371.499581569 m
Image shape: (720, 1280, 3)
 72%|███████▏  | 908/1261 [05:18<01:55,  3.07it/s]
542.58241518 m 531.203200516 m
Image shape: (720, 1280, 3)
 72%|███████▏  | 909/1261 [05:18<01:55,  3.06it/s]
776.747631919 m 523.401470062 m
Image shape: (720, 1280, 3)
 72%|███████▏  | 910/1261 [05:18<01:54,  3.06it/s]
918.419144569 m 366.84202128 m
Image shape: (720, 1280, 3)
 72%|███████▏  | 911/1261 [05:19<01:54,  3.06it/s]
903.909833485 m 381.527049885 m
Image shape: (720, 1280, 3)
 72%|███████▏  | 912/1261 [05:19<01:54,  3.04it/s]
1173.99353787 m 429.175291304 m
Image shape: (720, 1280, 3)
 72%|███████▏  | 913/1261 [05:19<01:54,  3.05it/s]
641.082547826 m 457.320652374 m
Image shape: (720, 1280, 3)
 72%|███████▏  | 914/1261 [05:20<01:53,  3.05it/s]
668.399971718 m 495.630026361 m
Image shape: (720, 1280, 3)
 73%|███████▎  | 915/1261 [05:20<01:53,  3.06it/s]
558.415248903 m 633.028502297 m
Image shape: (720, 1280, 3)
 73%|███████▎  | 916/1261 [05:20<01:52,  3.06it/s]
511.925252237 m 692.383126431 m
Image shape: (720, 1280, 3)
 73%|███████▎  | 917/1261 [05:21<01:52,  3.05it/s]
510.520888991 m 448.490935186 m
Image shape: (720, 1280, 3)
 73%|███████▎  | 918/1261 [05:21<01:52,  3.06it/s]
474.430646243 m 398.976615437 m
Image shape: (720, 1280, 3)
 73%|███████▎  | 919/1261 [05:21<01:51,  3.07it/s]
467.878194971 m 455.68938096 m
Image shape: (720, 1280, 3)
 73%|███████▎  | 920/1261 [05:22<01:51,  3.06it/s]
438.664388871 m 510.776353602 m
Image shape: (720, 1280, 3)
 73%|███████▎  | 921/1261 [05:22<01:51,  3.05it/s]
484.838374383 m 745.485911068 m
Image shape: (720, 1280, 3)
 73%|███████▎  | 922/1261 [05:22<01:51,  3.04it/s]
575.357983503 m 564.159579812 m
Image shape: (720, 1280, 3)
 73%|███████▎  | 923/1261 [05:23<01:51,  3.04it/s]
582.958928001 m 421.353557105 m
Image shape: (720, 1280, 3)
 73%|███████▎  | 924/1261 [05:23<01:50,  3.04it/s]
544.928545851 m 493.392970294 m
Image shape: (720, 1280, 3)
 73%|███████▎  | 925/1261 [05:23<01:50,  3.04it/s]
487.645764603 m 694.339083161 m
Image shape: (720, 1280, 3)
 73%|███████▎  | 926/1261 [05:23<01:50,  3.02it/s]
495.507373317 m 1106.20057496 m
Image shape: (720, 1280, 3)
 74%|███████▎  | 927/1261 [05:24<01:50,  3.02it/s]
334.555016171 m 958.301721881 m
Image shape: (720, 1280, 3)
 74%|███████▎  | 928/1261 [05:24<01:51,  2.99it/s]
437.88278886 m 563.021224477 m
Image shape: (720, 1280, 3)
 74%|███████▎  | 929/1261 [05:25<01:51,  2.98it/s]
402.983625271 m 469.518007014 m
Image shape: (720, 1280, 3)
 74%|███████▍  | 930/1261 [05:25<01:50,  2.99it/s]
440.93562628 m 400.233642325 m
Image shape: (720, 1280, 3)
 74%|███████▍  | 931/1261 [05:25<01:49,  3.00it/s]
629.27471773 m 398.53008417 m
Image shape: (720, 1280, 3)
 74%|███████▍  | 932/1261 [05:25<01:49,  3.02it/s]
726.98561281 m 415.751024928 m
Image shape: (720, 1280, 3)
 74%|███████▍  | 933/1261 [05:26<01:48,  3.02it/s]
705.344566965 m 842.093895805 m
Image shape: (720, 1280, 3)
 74%|███████▍  | 934/1261 [05:26<01:48,  3.03it/s]
563.586945404 m 502.542307318 m
Image shape: (720, 1280, 3)
 74%|███████▍  | 935/1261 [05:26<01:47,  3.04it/s]
647.224915629 m 763.469398797 m
Image shape: (720, 1280, 3)
 74%|███████▍  | 936/1261 [05:27<01:47,  3.04it/s]
564.439406966 m 1117.76225848 m
Image shape: (720, 1280, 3)
 74%|███████▍  | 937/1261 [05:27<01:46,  3.04it/s]
650.25346064 m 938.526077137 m
Image shape: (720, 1280, 3)
 74%|███████▍  | 938/1261 [05:27<01:45,  3.06it/s]
552.245439197 m 556.188988456 m
Image shape: (720, 1280, 3)
 74%|███████▍  | 939/1261 [05:28<01:45,  3.06it/s]
604.008604171 m 573.560574308 m
Image shape: (720, 1280, 3)
 75%|███████▍  | 940/1261 [05:28<01:45,  3.05it/s]
557.647905238 m 421.230239983 m
Image shape: (720, 1280, 3)
 75%|███████▍  | 941/1261 [05:28<01:45,  3.04it/s]
642.793851606 m 293.844854137 m
Image shape: (720, 1280, 3)
 75%|███████▍  | 942/1261 [05:29<01:45,  3.03it/s]
479.500652037 m 391.865901092 m
Image shape: (720, 1280, 3)
 75%|███████▍  | 943/1261 [05:29<01:45,  3.01it/s]
475.219049144 m 386.08481925 m
Image shape: (720, 1280, 3)
 75%|███████▍  | 944/1261 [05:29<01:45,  2.99it/s]
615.839019713 m 310.344337037 m
Image shape: (720, 1280, 3)
 75%|███████▍  | 945/1261 [05:30<01:45,  2.99it/s]
460.599053126 m 418.97777361 m
Image shape: (720, 1280, 3)
 75%|███████▌  | 946/1261 [05:30<01:44,  3.01it/s]
526.547787054 m 287.401098423 m
Image shape: (720, 1280, 3)
 75%|███████▌  | 947/1261 [05:30<01:44,  3.01it/s]
416.945717114 m 307.431886465 m
Image shape: (720, 1280, 3)
 75%|███████▌  | 948/1261 [05:31<01:43,  3.02it/s]
475.625777266 m 367.385434048 m
Image shape: (720, 1280, 3)
 75%|███████▌  | 949/1261 [05:31<01:43,  3.01it/s]
501.012587612 m 376.370118892 m
Image shape: (720, 1280, 3)
 75%|███████▌  | 950/1261 [05:31<01:42,  3.03it/s]
578.292023981 m 650.117438583 m
Image shape: (720, 1280, 3)
 75%|███████▌  | 951/1261 [05:32<01:41,  3.04it/s]
577.583422973 m 1046.69835936 m
Image shape: (720, 1280, 3)
 75%|███████▌  | 952/1261 [05:32<01:42,  3.03it/s]
660.965789458 m 402.325031327 m
Image shape: (720, 1280, 3)
 76%|███████▌  | 953/1261 [05:32<01:41,  3.02it/s]
631.80581863 m 332.112356443 m
Image shape: (720, 1280, 3)
 76%|███████▌  | 954/1261 [05:33<01:41,  3.04it/s]
685.753403009 m 334.524373858 m
Image shape: (720, 1280, 3)
 76%|███████▌  | 955/1261 [05:33<01:40,  3.05it/s]
729.658552084 m 363.115758426 m
Image shape: (720, 1280, 3)
 76%|███████▌  | 956/1261 [05:33<01:40,  3.05it/s]
670.738982647 m 379.219196161 m
Image shape: (720, 1280, 3)
 76%|███████▌  | 957/1261 [05:34<01:39,  3.06it/s]
469.008822912 m 346.203116613 m
Image shape: (720, 1280, 3)
 76%|███████▌  | 958/1261 [05:34<01:39,  3.06it/s]
441.37355496 m 375.168327709 m
Image shape: (720, 1280, 3)
 76%|███████▌  | 959/1261 [05:34<01:39,  3.05it/s]
459.11946269 m 385.611858134 m
Image shape: (720, 1280, 3)
 76%|███████▌  | 960/1261 [05:35<01:38,  3.04it/s]
340.667716954 m 721.942556805 m
Image shape: (720, 1280, 3)
 76%|███████▌  | 961/1261 [05:35<01:39,  3.02it/s]
432.324273187 m 1253.84839721 m
Image shape: (720, 1280, 3)
 76%|███████▋  | 962/1261 [05:35<01:39,  3.02it/s]
412.122479209 m 1505.65900865 m
Image shape: (720, 1280, 3)
 76%|███████▋  | 963/1261 [05:36<01:38,  3.02it/s]
390.262004577 m 512.381056868 m
Image shape: (720, 1280, 3)
 76%|███████▋  | 964/1261 [05:36<01:37,  3.04it/s]
301.981972625 m 454.647973446 m
Image shape: (720, 1280, 3)
 77%|███████▋  | 965/1261 [05:36<01:37,  3.03it/s]
285.742524099 m 427.38397169 m
Image shape: (720, 1280, 3)
 77%|███████▋  | 966/1261 [05:37<01:36,  3.04it/s]
264.93526666 m 445.798174372 m
Image shape: (720, 1280, 3)
 77%|███████▋  | 967/1261 [05:37<01:36,  3.05it/s]
350.929319462 m 471.578159974 m
Image shape: (720, 1280, 3)
 77%|███████▋  | 968/1261 [05:37<01:35,  3.05it/s]
372.76316976 m 514.341751074 m
Image shape: (720, 1280, 3)
 77%|███████▋  | 969/1261 [05:38<01:35,  3.05it/s]
459.579023188 m 333.020256557 m
Image shape: (720, 1280, 3)
 77%|███████▋  | 970/1261 [05:38<01:35,  3.05it/s]
345.462674933 m 338.312038958 m
Image shape: (720, 1280, 3)
 77%|███████▋  | 971/1261 [05:38<01:35,  3.05it/s]
374.479221025 m 604.494710274 m
Image shape: (720, 1280, 3)
 77%|███████▋  | 972/1261 [05:39<01:34,  3.05it/s]
327.481527998 m 362.674314766 m
Image shape: (720, 1280, 3)
 77%|███████▋  | 973/1261 [05:39<01:34,  3.04it/s]
321.805444071 m 508.961179971 m
Image shape: (720, 1280, 3)
 77%|███████▋  | 974/1261 [05:39<01:34,  3.04it/s]
313.123355596 m 642.195840209 m
Image shape: (720, 1280, 3)
 77%|███████▋  | 975/1261 [05:40<01:34,  3.03it/s]
357.364772049 m 520.390165488 m
Image shape: (720, 1280, 3)
 77%|███████▋  | 976/1261 [05:40<01:33,  3.05it/s]
335.077894493 m 432.695391827 m
Image shape: (720, 1280, 3)
 77%|███████▋  | 977/1261 [05:40<01:33,  3.04it/s]
331.256840621 m 300.527436998 m
Image shape: (720, 1280, 3)
 78%|███████▊  | 978/1261 [05:41<01:32,  3.05it/s]
1481.3816743 m 375.998157352 m
Image shape: (720, 1280, 3)
 78%|███████▊  | 979/1261 [05:41<01:32,  3.05it/s]
902.011059984 m 392.640262369 m
Image shape: (720, 1280, 3)
 78%|███████▊  | 980/1261 [05:41<01:32,  3.05it/s]
1038.0884796 m 307.741838416 m
Image shape: (720, 1280, 3)
 78%|███████▊  | 981/1261 [05:42<01:35,  2.95it/s]
276.10219182 m 305.94006552 m
Image shape: (720, 1280, 3)
 78%|███████▊  | 982/1261 [05:42<01:33,  2.98it/s]
573.813502437 m 312.066832277 m
Image shape: (720, 1280, 3)
 78%|███████▊  | 983/1261 [05:42<01:32,  3.00it/s]
627.908420038 m 677.550212334 m
Image shape: (720, 1280, 3)
 78%|███████▊  | 984/1261 [05:43<01:32,  3.01it/s]
12500.800931 m 893.869984711 m
Image shape: (720, 1280, 3)
 78%|███████▊  | 985/1261 [05:43<01:31,  3.01it/s]
38583.5970302 m 466.602992115 m
Image shape: (720, 1280, 3)
 78%|███████▊  | 986/1261 [05:43<01:30,  3.03it/s]
609.256093532 m 291.786785037 m
Image shape: (720, 1280, 3)
 78%|███████▊  | 987/1261 [05:44<01:30,  3.04it/s]
914.194587336 m 225.766861513 m
Image shape: (720, 1280, 3)
 78%|███████▊  | 988/1261 [05:44<01:30,  3.03it/s]
431.599721479 m 376.712763523 m
Image shape: (720, 1280, 3)
 78%|███████▊  | 989/1261 [05:44<01:29,  3.02it/s]
204.616609461 m 816.814047649 m
Image shape: (720, 1280, 3)
 79%|███████▊  | 990/1261 [05:45<01:29,  3.03it/s]
405.336837874 m 416.752660428 m
Image shape: (720, 1280, 3)
 79%|███████▊  | 991/1261 [05:45<01:29,  3.03it/s]
922.211620706 m 168.514841341 m
Image shape: (720, 1280, 3)
 79%|███████▊  | 992/1261 [05:45<01:28,  3.04it/s]
3788.11592445 m 1595.57641695 m
Image shape: (720, 1280, 3)
 79%|███████▊  | 993/1261 [05:46<01:27,  3.05it/s]
1272.47210877 m 447.049095309 m
Image shape: (720, 1280, 3)
 79%|███████▉  | 994/1261 [05:46<01:27,  3.05it/s]
876.021135295 m 361.629229847 m
Image shape: (720, 1280, 3)
 79%|███████▉  | 995/1261 [05:46<01:27,  3.05it/s]
605.637184126 m 210.465676134 m
Image shape: (720, 1280, 3)
 79%|███████▉  | 996/1261 [05:47<01:26,  3.07it/s]
1813.01772932 m 141.898976253 m
Image shape: (720, 1280, 3)
 79%|███████▉  | 997/1261 [05:47<01:26,  3.06it/s]
260.442060372 m 257.066346522 m
Image shape: (720, 1280, 3)
 79%|███████▉  | 998/1261 [05:47<01:25,  3.07it/s]
450.575207661 m 416.191043157 m
Image shape: (720, 1280, 3)
 79%|███████▉  | 999/1261 [05:48<01:25,  3.06it/s]
292.260696871 m 431.475479832 m
Image shape: (720, 1280, 3)
 79%|███████▉  | 1000/1261 [05:48<01:24,  3.08it/s]
233.038237828 m 261.810105222 m
Image shape: (720, 1280, 3)
 79%|███████▉  | 1001/1261 [05:48<01:23,  3.12it/s]
334.087643619 m 123.918137599 m
Image shape: (720, 1280, 3)
 79%|███████▉  | 1002/1261 [05:49<01:21,  3.17it/s]
386.143893383 m 99.2134261385 m
Image shape: (720, 1280, 3)
 80%|███████▉  | 1003/1261 [05:49<01:20,  3.20it/s]
420.707792268 m 103.813095713 m
Image shape: (720, 1280, 3)
 80%|███████▉  | 1004/1261 [05:49<01:19,  3.22it/s]
1408.69337865 m 108.430310737 m
Image shape: (720, 1280, 3)
 80%|███████▉  | 1005/1261 [05:49<01:20,  3.19it/s]
737.992500058 m 233.8006844 m
Image shape: (720, 1280, 3)
 80%|███████▉  | 1006/1261 [05:50<01:21,  3.13it/s]
2462.09631378 m 289.733248779 m
Image shape: (720, 1280, 3)
 80%|███████▉  | 1007/1261 [05:50<01:21,  3.10it/s]
3215.60900543 m 256.912561198 m
Image shape: (720, 1280, 3)
 80%|███████▉  | 1008/1261 [05:50<01:20,  3.15it/s]
183.560790689 m 243.357410336 m
Image shape: (720, 1280, 3)
 80%|████████  | 1009/1261 [05:51<01:19,  3.18it/s]
1664.02232484 m 205.905879269 m
Image shape: (720, 1280, 3)
 80%|████████  | 1010/1261 [05:51<01:18,  3.19it/s]
197.805936104 m 343.614044414 m
Image shape: (720, 1280, 3)
 80%|████████  | 1011/1261 [05:51<01:17,  3.23it/s]
453.777683015 m 290.79383755 m
Image shape: (720, 1280, 3)
 80%|████████  | 1012/1261 [05:52<01:16,  3.23it/s]
647.958799266 m 517.93663354 m
Image shape: (720, 1280, 3)
 80%|████████  | 1013/1261 [05:52<01:16,  3.24it/s]
347.918930882 m 161.276706037 m
Image shape: (720, 1280, 3)
 80%|████████  | 1014/1261 [05:52<01:16,  3.24it/s]
456.298617898 m 175.80337804 m
Image shape: (720, 1280, 3)
 80%|████████  | 1015/1261 [05:53<01:15,  3.26it/s]
347.952488583 m 102.941166359 m
Image shape: (720, 1280, 3)
 81%|████████  | 1016/1261 [05:53<01:15,  3.25it/s]
966.047878906 m 60.9003908471 m
Image shape: (720, 1280, 3)
 81%|████████  | 1017/1261 [05:53<01:14,  3.27it/s]
229.835834081 m 71.228875224 m
Image shape: (720, 1280, 3)
 81%|████████  | 1018/1261 [05:53<01:14,  3.26it/s]
323.576930486 m 115.801494183 m
Image shape: (720, 1280, 3)
 81%|████████  | 1019/1261 [05:54<01:14,  3.25it/s]
216.962920181 m 194.257552143 m
Image shape: (720, 1280, 3)
 81%|████████  | 1020/1261 [05:54<01:15,  3.21it/s]
595.457384188 m 234.331405046 m
Image shape: (720, 1280, 3)
 81%|████████  | 1021/1261 [05:54<01:14,  3.23it/s]
1577.27655305 m 148.273007528 m
Image shape: (720, 1280, 3)
 81%|████████  | 1022/1261 [05:55<01:14,  3.20it/s]
285.82676561 m 256.616041979 m
Image shape: (720, 1280, 3)
 81%|████████  | 1023/1261 [05:55<01:14,  3.19it/s]
239.722869831 m 302.161793245 m
Image shape: (720, 1280, 3)
 81%|████████  | 1024/1261 [05:55<01:14,  3.19it/s]
854.634266686 m 380.055308435 m
Image shape: (720, 1280, 3)
 81%|████████▏ | 1025/1261 [05:56<01:13,  3.22it/s]
3455.54851391 m 355.739235122 m
Image shape: (720, 1280, 3)
 81%|████████▏ | 1026/1261 [05:56<01:12,  3.23it/s]
1105.7093996 m 319.480365708 m
Image shape: (720, 1280, 3)
 81%|████████▏ | 1027/1261 [05:56<01:11,  3.25it/s]
736.849162863 m 232.659970739 m
Image shape: (720, 1280, 3)
 82%|████████▏ | 1028/1261 [05:57<01:11,  3.26it/s]
17268.4785119 m 271.103320558 m
Image shape: (720, 1280, 3)
 82%|████████▏ | 1029/1261 [05:57<01:11,  3.24it/s]
3175.0911821 m 235.962472406 m
Image shape: (720, 1280, 3)
 82%|████████▏ | 1030/1261 [05:57<01:11,  3.23it/s]
263.321933104 m 306.413442328 m
Image shape: (720, 1280, 3)
 82%|████████▏ | 1031/1261 [05:58<01:11,  3.23it/s]
1262.65325924 m 353.817512113 m
Image shape: (720, 1280, 3)
 82%|████████▏ | 1032/1261 [05:58<01:11,  3.22it/s]
266.087395656 m 395.751663391 m
Image shape: (720, 1280, 3)
 82%|████████▏ | 1033/1261 [05:58<01:11,  3.21it/s]
152.750640742 m 478.378917718 m
Image shape: (720, 1280, 3)
 82%|████████▏ | 1034/1261 [05:58<01:11,  3.19it/s]
215.193215776 m 1024.94728913 m
Image shape: (720, 1280, 3)
 82%|████████▏ | 1035/1261 [05:59<01:11,  3.14it/s]
141.536414034 m 458.987539444 m
Image shape: (720, 1280, 3)
 82%|████████▏ | 1036/1261 [05:59<01:12,  3.11it/s]
142.052392317 m 296.320037897 m
Image shape: (720, 1280, 3)
 82%|████████▏ | 1037/1261 [05:59<01:12,  3.09it/s]
143.491042774 m 184.156948389 m
Image shape: (720, 1280, 3)
 82%|████████▏ | 1038/1261 [06:00<01:12,  3.07it/s]
189.405813218 m 167.160102929 m
Image shape: (720, 1280, 3)
 82%|████████▏ | 1039/1261 [06:00<01:12,  3.05it/s]
390.477178641 m 138.719548263 m
Image shape: (720, 1280, 3)
 82%|████████▏ | 1040/1261 [06:00<01:12,  3.04it/s]
1242.78558003 m 103.32614149 m
Image shape: (720, 1280, 3)
 83%|████████▎ | 1041/1261 [06:01<01:12,  3.02it/s]
120.426167013 m 146.486507344 m
Image shape: (720, 1280, 3)
 83%|████████▎ | 1042/1261 [06:01<01:12,  3.00it/s]
125.784300621 m 222.225429123 m
Image shape: (720, 1280, 3)
 83%|████████▎ | 1043/1261 [06:01<01:11,  3.03it/s]
122.299331503 m 145.580220366 m
Image shape: (720, 1280, 3)
 83%|████████▎ | 1044/1261 [06:02<01:11,  3.03it/s]
95.0149537953 m 122.947121674 m
Image shape: (720, 1280, 3)
 83%|████████▎ | 1045/1261 [06:02<01:11,  3.01it/s]
225.319942098 m 231.017680605 m
Image shape: (720, 1280, 3)
 83%|████████▎ | 1046/1261 [06:02<01:11,  3.02it/s]
210.102424196 m 491.700064407 m
Image shape: (720, 1280, 3)
 83%|████████▎ | 1047/1261 [06:03<01:11,  2.98it/s]
146.465901202 m 343.138235635 m
Image shape: (720, 1280, 3)
 83%|████████▎ | 1048/1261 [06:03<01:10,  3.00it/s]
205.966474236 m 355.494339821 m
Image shape: (720, 1280, 3)
 83%|████████▎ | 1049/1261 [06:03<01:11,  2.98it/s]
256.372932806 m 283.389039569 m
Image shape: (720, 1280, 3)
 83%|████████▎ | 1050/1261 [06:04<01:10,  3.00it/s]
255.906957589 m 332.764530219 m
Image shape: (720, 1280, 3)
 83%|████████▎ | 1051/1261 [06:04<01:09,  3.01it/s]
271.850925482 m 325.854992018 m
Image shape: (720, 1280, 3)
 83%|████████▎ | 1052/1261 [06:04<01:08,  3.03it/s]
275.486549862 m 351.403482913 m
Image shape: (720, 1280, 3)
 84%|████████▎ | 1053/1261 [06:05<01:08,  3.03it/s]
3625.07500221 m 246.123753057 m
Image shape: (720, 1280, 3)
 84%|████████▎ | 1054/1261 [06:05<01:13,  2.82it/s]
440.028782097 m 200.493419873 m
Image shape: (720, 1280, 3)
 84%|████████▎ | 1055/1261 [06:06<01:19,  2.60it/s]
545.550903881 m 252.637240946 m
Image shape: (720, 1280, 3)
 84%|████████▎ | 1056/1261 [06:06<01:16,  2.69it/s]
1090.99057568 m 782.985181622 m
Image shape: (720, 1280, 3)
 84%|████████▍ | 1057/1261 [06:06<01:13,  2.77it/s]
531.396095182 m 1133.73968734 m
Image shape: (720, 1280, 3)
 84%|████████▍ | 1058/1261 [06:07<01:11,  2.84it/s]
442.461899436 m 619.859975097 m
Image shape: (720, 1280, 3)
 84%|████████▍ | 1059/1261 [06:07<01:10,  2.88it/s]
455.0754379 m 586.069258243 m
Image shape: (720, 1280, 3)
 84%|████████▍ | 1060/1261 [06:07<01:09,  2.91it/s]
496.892757376 m 500.780089089 m
Image shape: (720, 1280, 3)
 84%|████████▍ | 1061/1261 [06:08<01:08,  2.92it/s]
473.641783201 m 596.392331519 m
Image shape: (720, 1280, 3)
 84%|████████▍ | 1062/1261 [06:08<01:08,  2.92it/s]
600.335858304 m 661.733595929 m
Image shape: (720, 1280, 3)
 84%|████████▍ | 1063/1261 [06:08<01:07,  2.91it/s]
658.865695312 m 686.675321032 m
Image shape: (720, 1280, 3)
 84%|████████▍ | 1064/1261 [06:09<01:07,  2.92it/s]
563.313870707 m 401.078411907 m
Image shape: (720, 1280, 3)
 84%|████████▍ | 1065/1261 [06:09<01:06,  2.96it/s]
666.775136769 m 424.433000387 m
Image shape: (720, 1280, 3)
 85%|████████▍ | 1066/1261 [06:09<01:05,  2.99it/s]
661.643846209 m 405.944501043 m
Image shape: (720, 1280, 3)
 85%|████████▍ | 1067/1261 [06:10<01:05,  2.95it/s]
606.232673525 m 514.044691113 m
Image shape: (720, 1280, 3)
 85%|████████▍ | 1068/1261 [06:10<01:05,  2.95it/s]
658.095730287 m 658.02016185 m
Image shape: (720, 1280, 3)
 85%|████████▍ | 1069/1261 [06:10<01:04,  2.96it/s]
624.755606677 m 659.907496574 m
Image shape: (720, 1280, 3)
 85%|████████▍ | 1070/1261 [06:11<01:04,  2.97it/s]
520.7803892 m 466.153103804 m
Image shape: (720, 1280, 3)
 85%|████████▍ | 1071/1261 [06:11<01:03,  2.98it/s]
550.746639403 m 455.612838173 m
Image shape: (720, 1280, 3)
 85%|████████▌ | 1072/1261 [06:11<01:04,  2.95it/s]
721.06726968 m 431.682821609 m
Image shape: (720, 1280, 3)
 85%|████████▌ | 1073/1261 [06:12<01:04,  2.93it/s]
761.718597861 m 318.908116394 m
Image shape: (720, 1280, 3)
 85%|████████▌ | 1074/1261 [06:12<01:04,  2.92it/s]
697.720237927 m 549.541397657 m
Image shape: (720, 1280, 3)
 85%|████████▌ | 1075/1261 [06:12<01:03,  2.93it/s]
981.52174244 m 635.285613701 m
Image shape: (720, 1280, 3)
 85%|████████▌ | 1076/1261 [06:13<01:02,  2.94it/s]
953.245830745 m 357.175925839 m
Image shape: (720, 1280, 3)
 85%|████████▌ | 1077/1261 [06:13<01:02,  2.95it/s]
562.221550041 m 395.39062366 m
Image shape: (720, 1280, 3)
 85%|████████▌ | 1078/1261 [06:13<01:01,  2.95it/s]
645.902558302 m 443.337584832 m
Image shape: (720, 1280, 3)
 86%|████████▌ | 1079/1261 [06:14<01:01,  2.95it/s]
503.936171325 m 538.470795327 m
Image shape: (720, 1280, 3)
 86%|████████▌ | 1080/1261 [06:14<01:01,  2.95it/s]
496.193433939 m 557.508827679 m
Image shape: (720, 1280, 3)
 86%|████████▌ | 1081/1261 [06:14<01:00,  2.97it/s]
609.466384977 m 684.417933022 m
Image shape: (720, 1280, 3)
 86%|████████▌ | 1082/1261 [06:15<01:00,  2.98it/s]
504.216966886 m 467.154784837 m
Image shape: (720, 1280, 3)
 86%|████████▌ | 1083/1261 [06:15<00:59,  2.99it/s]
459.759692841 m 381.53012843 m
Image shape: (720, 1280, 3)
 86%|████████▌ | 1084/1261 [06:15<01:01,  2.89it/s]
501.643478196 m 348.78571697 m
Image shape: (720, 1280, 3)
 86%|████████▌ | 1085/1261 [06:16<01:00,  2.90it/s]
509.942356785 m 391.778622589 m
Image shape: (720, 1280, 3)
 86%|████████▌ | 1086/1261 [06:16<00:59,  2.93it/s]
542.234295074 m 420.991866804 m
Image shape: (720, 1280, 3)
 86%|████████▌ | 1087/1261 [06:16<00:59,  2.93it/s]
625.774155527 m 431.242337465 m
Image shape: (720, 1280, 3)
 86%|████████▋ | 1088/1261 [06:17<00:58,  2.95it/s]
911.487400241 m 401.966845948 m
Image shape: (720, 1280, 3)
 86%|████████▋ | 1089/1261 [06:17<00:57,  2.97it/s]
620.650916513 m 420.071922356 m
Image shape: (720, 1280, 3)
 86%|████████▋ | 1090/1261 [06:18<00:59,  2.87it/s]
613.683804401 m 417.656118334 m
Image shape: (720, 1280, 3)
 87%|████████▋ | 1091/1261 [06:18<01:00,  2.82it/s]
751.782195354 m 499.585918288 m
Image shape: (720, 1280, 3)
 87%|████████▋ | 1092/1261 [06:18<01:00,  2.81it/s]
596.005838612 m 528.985673443 m
Image shape: (720, 1280, 3)
 87%|████████▋ | 1093/1261 [06:19<00:58,  2.86it/s]
568.363659844 m 483.698177327 m
Image shape: (720, 1280, 3)
 87%|████████▋ | 1094/1261 [06:19<00:57,  2.90it/s]
723.800418785 m 388.736728647 m
Image shape: (720, 1280, 3)
 87%|████████▋ | 1095/1261 [06:19<00:56,  2.93it/s]
592.309971798 m 333.712512409 m
Image shape: (720, 1280, 3)
 87%|████████▋ | 1096/1261 [06:20<00:55,  2.95it/s]
759.368086623 m 370.495248592 m
Image shape: (720, 1280, 3)
 87%|████████▋ | 1097/1261 [06:20<00:55,  2.96it/s]
819.778371446 m 397.495848081 m
Image shape: (720, 1280, 3)
 87%|████████▋ | 1098/1261 [06:20<00:55,  2.95it/s]
892.473302625 m 423.386018654 m
Image shape: (720, 1280, 3)
 87%|████████▋ | 1099/1261 [06:21<00:55,  2.93it/s]
842.26315898 m 371.335898003 m
Image shape: (720, 1280, 3)
 87%|████████▋ | 1100/1261 [06:21<00:54,  2.94it/s]
744.040359636 m 417.800967176 m
Image shape: (720, 1280, 3)
 87%|████████▋ | 1101/1261 [06:21<00:54,  2.96it/s]
804.996470339 m 485.844572223 m
Image shape: (720, 1280, 3)
 87%|████████▋ | 1102/1261 [06:22<00:53,  2.95it/s]
850.943208977 m 594.105511264 m
Image shape: (720, 1280, 3)
 87%|████████▋ | 1103/1261 [06:22<00:53,  2.95it/s]
840.954443274 m 786.326651009 m
Image shape: (720, 1280, 3)
 88%|████████▊ | 1104/1261 [06:22<00:53,  2.95it/s]
1173.16507223 m 1826.05942043 m
Image shape: (720, 1280, 3)
 88%|████████▊ | 1105/1261 [06:23<00:52,  2.95it/s]
1403.50065445 m 517.454434561 m
Image shape: (720, 1280, 3)
 88%|████████▊ | 1106/1261 [06:23<00:52,  2.95it/s]
992.300351592 m 503.681673557 m
Image shape: (720, 1280, 3)
 88%|████████▊ | 1107/1261 [06:23<00:51,  2.98it/s]
1101.25524548 m 373.26687434 m
Image shape: (720, 1280, 3)
 88%|████████▊ | 1108/1261 [06:24<00:51,  2.99it/s]
999.758885846 m 550.063657091 m
Image shape: (720, 1280, 3)
 88%|████████▊ | 1109/1261 [06:24<00:50,  3.00it/s]
1153.37071995 m 666.593398216 m
Image shape: (720, 1280, 3)
 88%|████████▊ | 1110/1261 [06:24<00:50,  3.01it/s]
810.167265315 m 605.51392693 m
Image shape: (720, 1280, 3)
 88%|████████▊ | 1111/1261 [06:25<00:49,  3.02it/s]
663.355601516 m 397.152786265 m
Image shape: (720, 1280, 3)
 88%|████████▊ | 1112/1261 [06:25<00:49,  3.00it/s]
693.947068712 m 391.197622619 m
Image shape: (720, 1280, 3)
 88%|████████▊ | 1113/1261 [06:25<00:49,  3.00it/s]
683.950180626 m 404.425671166 m
Image shape: (720, 1280, 3)
 88%|████████▊ | 1114/1261 [06:26<00:49,  2.98it/s]
664.885222671 m 441.907770527 m
Image shape: (720, 1280, 3)
 88%|████████▊ | 1115/1261 [06:26<00:49,  2.95it/s]
689.470518287 m 431.906028792 m
Image shape: (720, 1280, 3)
 89%|████████▊ | 1116/1261 [06:26<00:48,  2.96it/s]
627.716129956 m 451.870971636 m
Image shape: (720, 1280, 3)
 89%|████████▊ | 1117/1261 [06:27<00:48,  2.96it/s]
825.562559947 m 451.194161175 m
Image shape: (720, 1280, 3)
 89%|████████▊ | 1118/1261 [06:27<00:48,  2.96it/s]
862.590267705 m 447.02365033 m
Image shape: (720, 1280, 3)
 89%|████████▊ | 1119/1261 [06:27<00:48,  2.95it/s]
1040.18907755 m 480.945680382 m
Image shape: (720, 1280, 3)
 89%|████████▉ | 1120/1261 [06:28<00:47,  2.96it/s]
1328.70682247 m 374.941538427 m
Image shape: (720, 1280, 3)
 89%|████████▉ | 1121/1261 [06:28<00:47,  2.98it/s]
1486.3782895 m 525.831849998 m
Image shape: (720, 1280, 3)
 89%|████████▉ | 1122/1261 [06:28<00:46,  2.98it/s]
1461.68757675 m 372.212010032 m
Image shape: (720, 1280, 3)
 89%|████████▉ | 1123/1261 [06:29<00:46,  2.95it/s]
1986.36961714 m 403.048763372 m
Image shape: (720, 1280, 3)
 89%|████████▉ | 1124/1261 [06:29<00:46,  2.97it/s]
1849.83631116 m 445.46637032 m
Image shape: (720, 1280, 3)
 89%|████████▉ | 1125/1261 [06:29<00:45,  2.99it/s]
1024.63791591 m 552.644585309 m
Image shape: (720, 1280, 3)
 89%|████████▉ | 1126/1261 [06:30<00:45,  2.98it/s]
993.493390354 m 976.351168351 m
Image shape: (720, 1280, 3)
 89%|████████▉ | 1127/1261 [06:30<00:44,  2.98it/s]
1296.46783685 m 1073.30889917 m
Image shape: (720, 1280, 3)
 89%|████████▉ | 1128/1261 [06:30<00:44,  2.99it/s]
873.403202082 m 339.15203665 m
Image shape: (720, 1280, 3)
 90%|████████▉ | 1129/1261 [06:31<00:44,  2.99it/s]
1021.52723302 m 472.240747088 m
Image shape: (720, 1280, 3)
 90%|████████▉ | 1130/1261 [06:31<00:44,  2.97it/s]
889.709300496 m 510.748834721 m
Image shape: (720, 1280, 3)
 90%|████████▉ | 1131/1261 [06:31<00:43,  2.98it/s]
785.519753274 m 584.285619608 m
Image shape: (720, 1280, 3)
 90%|████████▉ | 1132/1261 [06:32<00:43,  2.98it/s]
616.997720628 m 727.040395987 m
Image shape: (720, 1280, 3)
 90%|████████▉ | 1133/1261 [06:32<00:42,  2.99it/s]
599.357082966 m 401.384363199 m
Image shape: (720, 1280, 3)
 90%|████████▉ | 1134/1261 [06:32<00:42,  2.97it/s]
571.227384686 m 400.128649898 m
Image shape: (720, 1280, 3)
 90%|█████████ | 1135/1261 [06:33<00:42,  2.95it/s]
571.02983607 m 466.630910098 m
Image shape: (720, 1280, 3)
 90%|█████████ | 1136/1261 [06:33<00:42,  2.98it/s]
640.826160899 m 528.773993867 m
Image shape: (720, 1280, 3)
 90%|█████████ | 1137/1261 [06:33<00:41,  2.97it/s]
755.090310814 m 896.86840956 m
Image shape: (720, 1280, 3)
 90%|█████████ | 1138/1261 [06:34<00:41,  2.96it/s]
621.184591137 m 813.533042784 m
Image shape: (720, 1280, 3)
 90%|█████████ | 1139/1261 [06:34<00:41,  2.97it/s]
703.528471449 m 570.89197696 m
Image shape: (720, 1280, 3)
 90%|█████████ | 1140/1261 [06:34<00:40,  2.99it/s]
883.514314759 m 550.198906355 m
Image shape: (720, 1280, 3)
 90%|█████████ | 1141/1261 [06:35<00:39,  3.01it/s]
1050.19305139 m 519.665757301 m
Image shape: (720, 1280, 3)
 91%|█████████ | 1142/1261 [06:35<00:39,  3.02it/s]
1101.93830045 m 589.860145961 m
Image shape: (720, 1280, 3)
 91%|█████████ | 1143/1261 [06:35<00:38,  3.04it/s]
1072.12506818 m 732.637713729 m
Image shape: (720, 1280, 3)
 91%|█████████ | 1144/1261 [06:36<00:38,  3.04it/s]
1143.22137525 m 781.03607772 m
Image shape: (720, 1280, 3)
 91%|█████████ | 1145/1261 [06:36<00:38,  3.05it/s]
1093.73195725 m 795.690708527 m
Image shape: (720, 1280, 3)
 91%|█████████ | 1146/1261 [06:36<00:37,  3.05it/s]
874.858983036 m 716.725351759 m
Image shape: (720, 1280, 3)
 91%|█████████ | 1147/1261 [06:37<00:37,  3.08it/s]
1023.54719091 m 1115.8635573 m
Image shape: (720, 1280, 3)
 91%|█████████ | 1148/1261 [06:37<00:36,  3.08it/s]
837.533400695 m 545.97454348 m
Image shape: (720, 1280, 3)
 91%|█████████ | 1149/1261 [06:37<00:36,  3.06it/s]
931.508219725 m 430.644605209 m
Image shape: (720, 1280, 3)
 91%|█████████ | 1150/1261 [06:38<00:36,  3.06it/s]
889.671704357 m 667.37543253 m
Image shape: (720, 1280, 3)
 91%|█████████▏| 1151/1261 [06:38<00:35,  3.06it/s]
708.813272888 m 503.451248651 m
Image shape: (720, 1280, 3)
 91%|█████████▏| 1152/1261 [06:38<00:35,  3.05it/s]
717.641924008 m 459.924017754 m
Image shape: (720, 1280, 3)
 91%|█████████▏| 1153/1261 [06:39<00:35,  3.04it/s]
712.175934806 m 535.601254295 m
Image shape: (720, 1280, 3)
 92%|█████████▏| 1154/1261 [06:39<00:35,  3.05it/s]
797.788683873 m 408.037329883 m
Image shape: (720, 1280, 3)
 92%|█████████▏| 1155/1261 [06:39<00:34,  3.04it/s]
868.65117303 m 572.492268763 m
Image shape: (720, 1280, 3)
 92%|█████████▏| 1156/1261 [06:40<00:34,  3.01it/s]
783.896721894 m 377.061688744 m
Image shape: (720, 1280, 3)
 92%|█████████▏| 1157/1261 [06:40<00:34,  3.01it/s]
780.672992985 m 347.787361601 m
Image shape: (720, 1280, 3)
 92%|█████████▏| 1158/1261 [06:40<00:34,  3.01it/s]
663.446907653 m 427.403632513 m
Image shape: (720, 1280, 3)
 92%|█████████▏| 1159/1261 [06:41<00:33,  3.03it/s]
634.049744318 m 657.543909249 m
Image shape: (720, 1280, 3)
 92%|█████████▏| 1160/1261 [06:41<00:33,  2.97it/s]
937.302783033 m 943.866677868 m
Image shape: (720, 1280, 3)
 92%|█████████▏| 1161/1261 [06:41<00:34,  2.93it/s]
1110.72492903 m 613.811800725 m
Image shape: (720, 1280, 3)
 92%|█████████▏| 1162/1261 [06:42<00:33,  2.95it/s]
791.405742646 m 466.244122495 m
Image shape: (720, 1280, 3)
 92%|█████████▏| 1163/1261 [06:42<00:32,  2.98it/s]
557.613316579 m 405.972026149 m
Image shape: (720, 1280, 3)
 92%|█████████▏| 1164/1261 [06:42<00:32,  2.99it/s]
520.717266288 m 425.196353619 m
Image shape: (720, 1280, 3)
 92%|█████████▏| 1165/1261 [06:43<00:32,  3.00it/s]
478.788271748 m 382.128096811 m
Image shape: (720, 1280, 3)
 92%|█████████▏| 1166/1261 [06:43<00:31,  3.00it/s]
507.796763859 m 554.024533471 m
Image shape: (720, 1280, 3)
 93%|█████████▎| 1167/1261 [06:43<00:31,  3.00it/s]
496.840958707 m 468.261455328 m
Image shape: (720, 1280, 3)
 93%|█████████▎| 1168/1261 [06:44<00:30,  3.01it/s]
455.21946369 m 349.940509283 m
Image shape: (720, 1280, 3)
 93%|█████████▎| 1169/1261 [06:44<00:30,  2.98it/s]
462.641187422 m 660.582647129 m
Image shape: (720, 1280, 3)
 93%|█████████▎| 1170/1261 [06:44<00:30,  2.99it/s]
409.482102551 m 907.395853884 m
Image shape: (720, 1280, 3)
 93%|█████████▎| 1171/1261 [06:45<00:30,  2.99it/s]
809.00916571 m 712.893760814 m
Image shape: (720, 1280, 3)
 93%|█████████▎| 1172/1261 [06:45<00:29,  2.99it/s]
565.237427112 m 819.163969842 m
Image shape: (720, 1280, 3)
 93%|█████████▎| 1173/1261 [06:45<00:29,  2.98it/s]
541.25980385 m 655.790832304 m
Image shape: (720, 1280, 3)
 93%|█████████▎| 1174/1261 [06:46<00:29,  2.97it/s]
534.741874536 m 377.634991985 m
Image shape: (720, 1280, 3)
 93%|█████████▎| 1175/1261 [06:46<00:29,  2.92it/s]
651.349873452 m 351.922346402 m
Image shape: (720, 1280, 3)
 93%|█████████▎| 1176/1261 [06:46<00:29,  2.91it/s]
771.703241628 m 378.747786578 m
Image shape: (720, 1280, 3)
 93%|█████████▎| 1177/1261 [06:47<00:28,  2.94it/s]
788.55498934 m 377.108777116 m
Image shape: (720, 1280, 3)
 93%|█████████▎| 1178/1261 [06:47<00:28,  2.96it/s]
1010.67160782 m 419.438787192 m
Image shape: (720, 1280, 3)
 93%|█████████▎| 1179/1261 [06:47<00:27,  2.98it/s]
1026.73008177 m 361.497149741 m
Image shape: (720, 1280, 3)
 94%|█████████▎| 1180/1261 [06:48<00:27,  2.99it/s]
616.779260745 m 440.182131202 m
Image shape: (720, 1280, 3)
 94%|█████████▎| 1181/1261 [06:48<00:26,  3.00it/s]
727.751546293 m 552.39146588 m
Image shape: (720, 1280, 3)
 94%|█████████▎| 1182/1261 [06:48<00:26,  3.01it/s]
812.831188916 m 822.247129984 m
Image shape: (720, 1280, 3)
 94%|█████████▍| 1183/1261 [06:49<00:25,  3.01it/s]
589.449178132 m 639.864617694 m
Image shape: (720, 1280, 3)
 94%|█████████▍| 1184/1261 [06:49<00:25,  3.02it/s]
632.750840556 m 434.264396591 m
Image shape: (720, 1280, 3)
 94%|█████████▍| 1185/1261 [06:49<00:25,  3.02it/s]
660.931832441 m 390.697301819 m
Image shape: (720, 1280, 3)
 94%|█████████▍| 1186/1261 [06:50<00:24,  3.03it/s]
700.560457895 m 275.619876231 m
Image shape: (720, 1280, 3)
 94%|█████████▍| 1187/1261 [06:50<00:24,  3.02it/s]
621.835187547 m 420.385690146 m
Image shape: (720, 1280, 3)
 94%|█████████▍| 1188/1261 [06:50<00:24,  3.01it/s]
774.97900414 m 434.854982339 m
Image shape: (720, 1280, 3)
 94%|█████████▍| 1189/1261 [06:51<00:23,  3.02it/s]
864.328981033 m 471.221950009 m
Image shape: (720, 1280, 3)
 94%|█████████▍| 1190/1261 [06:51<00:23,  3.01it/s]
626.655116707 m 396.492283182 m
Image shape: (720, 1280, 3)
 94%|█████████▍| 1191/1261 [06:51<00:23,  3.00it/s]
556.225003723 m 562.681369766 m
Image shape: (720, 1280, 3)
 95%|█████████▍| 1192/1261 [06:52<00:23,  2.98it/s]
512.197674581 m 629.687367192 m
Image shape: (720, 1280, 3)
 95%|█████████▍| 1193/1261 [06:52<00:22,  2.98it/s]
521.091536012 m 704.828860109 m
Image shape: (720, 1280, 3)
 95%|█████████▍| 1194/1261 [06:52<00:22,  2.98it/s]
416.672511685 m 532.299359327 m
Image shape: (720, 1280, 3)
 95%|█████████▍| 1195/1261 [06:53<00:22,  2.98it/s]
399.752883073 m 661.490994336 m
Image shape: (720, 1280, 3)
 95%|█████████▍| 1196/1261 [06:53<00:22,  2.95it/s]
380.519391879 m 440.698065538 m
Image shape: (720, 1280, 3)
 95%|█████████▍| 1197/1261 [06:53<00:21,  2.93it/s]
444.53688014 m 339.041988509 m
Image shape: (720, 1280, 3)
 95%|█████████▌| 1198/1261 [06:54<00:21,  2.88it/s]
545.895372247 m 365.351113168 m
Image shape: (720, 1280, 3)
 95%|█████████▌| 1199/1261 [06:54<00:21,  2.91it/s]
722.933859751 m 398.383678847 m
Image shape: (720, 1280, 3)
 95%|█████████▌| 1200/1261 [06:54<00:20,  2.95it/s]
897.952014187 m 427.083807583 m
Image shape: (720, 1280, 3)
 95%|█████████▌| 1201/1261 [06:55<00:20,  2.98it/s]
809.657335273 m 377.329369879 m
Image shape: (720, 1280, 3)
 95%|█████████▌| 1202/1261 [06:55<00:19,  2.98it/s]
770.301522235 m 435.608117441 m
Image shape: (720, 1280, 3)
 95%|█████████▌| 1203/1261 [06:55<00:19,  2.97it/s]
735.818548386 m 593.762132696 m
Image shape: (720, 1280, 3)
 95%|█████████▌| 1204/1261 [06:56<00:19,  2.95it/s]
689.100971354 m 584.584712088 m
Image shape: (720, 1280, 3)
 96%|█████████▌| 1205/1261 [06:56<00:19,  2.93it/s]
442.58240869 m 651.373145982 m
Image shape: (720, 1280, 3)
 96%|█████████▌| 1206/1261 [06:56<00:18,  2.93it/s]
463.466463239 m 497.964842871 m
Image shape: (720, 1280, 3)
 96%|█████████▌| 1207/1261 [06:57<00:18,  2.92it/s]
418.203003482 m 396.74604493 m
Image shape: (720, 1280, 3)
 96%|█████████▌| 1208/1261 [06:57<00:18,  2.92it/s]
482.964709083 m 279.691984525 m
Image shape: (720, 1280, 3)
 96%|█████████▌| 1209/1261 [06:57<00:17,  2.91it/s]
457.485021034 m 286.822101626 m
Image shape: (720, 1280, 3)
 96%|█████████▌| 1210/1261 [06:58<00:17,  2.94it/s]
444.861538624 m 332.028850877 m
Image shape: (720, 1280, 3)
 96%|█████████▌| 1211/1261 [06:58<00:16,  2.96it/s]
403.270146649 m 320.426397099 m
Image shape: (720, 1280, 3)
 96%|█████████▌| 1212/1261 [06:58<00:16,  2.95it/s]
397.476119744 m 281.27874133 m
Image shape: (720, 1280, 3)
 96%|█████████▌| 1213/1261 [06:59<00:16,  2.95it/s]
407.460481045 m 270.696621904 m
Image shape: (720, 1280, 3)
 96%|█████████▋| 1214/1261 [06:59<00:15,  2.94it/s]
382.357842568 m 306.856408738 m
Image shape: (720, 1280, 3)
 96%|█████████▋| 1215/1261 [06:59<00:15,  2.95it/s]
420.847162269 m 350.369060343 m
Image shape: (720, 1280, 3)
 96%|█████████▋| 1216/1261 [07:00<00:15,  2.94it/s]
438.694927397 m 401.808417762 m
Image shape: (720, 1280, 3)
 97%|█████████▋| 1217/1261 [07:00<00:14,  2.94it/s]
401.764131081 m 513.437520944 m
Image shape: (720, 1280, 3)
 97%|█████████▋| 1218/1261 [07:01<00:14,  2.93it/s]
448.577868817 m 391.617652727 m
Image shape: (720, 1280, 3)
 97%|█████████▋| 1219/1261 [07:01<00:14,  2.94it/s]
506.730075911 m 329.169738492 m
Image shape: (720, 1280, 3)
 97%|█████████▋| 1220/1261 [07:01<00:13,  2.95it/s]
676.479018063 m 320.627727169 m
Image shape: (720, 1280, 3)
 97%|█████████▋| 1221/1261 [07:02<00:13,  2.95it/s]
767.466017485 m 349.53530725 m
Image shape: (720, 1280, 3)
 97%|█████████▋| 1222/1261 [07:02<00:13,  2.97it/s]
653.074172586 m 362.493534197 m
Image shape: (720, 1280, 3)
 97%|█████████▋| 1223/1261 [07:02<00:12,  2.98it/s]
623.161219228 m 346.218969637 m
Image shape: (720, 1280, 3)
 97%|█████████▋| 1224/1261 [07:03<00:12,  2.96it/s]
531.723937055 m 345.326067452 m
Image shape: (720, 1280, 3)
 97%|█████████▋| 1225/1261 [07:03<00:12,  2.97it/s]
606.742527481 m 340.902364759 m
Image shape: (720, 1280, 3)
 97%|█████████▋| 1226/1261 [07:03<00:11,  2.97it/s]
751.438751098 m 427.299493572 m
Image shape: (720, 1280, 3)
 97%|█████████▋| 1227/1261 [07:04<00:11,  2.95it/s]
748.749851444 m 434.370770683 m
Image shape: (720, 1280, 3)
 97%|█████████▋| 1228/1261 [07:04<00:11,  2.97it/s]
673.784152802 m 420.273045075 m
Image shape: (720, 1280, 3)
 97%|█████████▋| 1229/1261 [07:04<00:10,  2.96it/s]
1134.03722271 m 263.136178939 m
Image shape: (720, 1280, 3)
 98%|█████████▊| 1230/1261 [07:05<00:10,  2.96it/s]
1154.91837209 m 319.270120736 m
Image shape: (720, 1280, 3)
 98%|█████████▊| 1231/1261 [07:05<00:10,  2.97it/s]
950.993463137 m 327.297765393 m
Image shape: (720, 1280, 3)
 98%|█████████▊| 1232/1261 [07:05<00:09,  2.97it/s]
1040.78119675 m 345.13752553 m
Image shape: (720, 1280, 3)
 98%|█████████▊| 1233/1261 [07:06<00:09,  2.98it/s]
1101.10156859 m 337.702689244 m
Image shape: (720, 1280, 3)
 98%|█████████▊| 1234/1261 [07:06<00:09,  2.97it/s]
949.397140496 m 392.243655276 m
Image shape: (720, 1280, 3)
 98%|█████████▊| 1235/1261 [07:06<00:08,  3.00it/s]
980.063787825 m 335.314284602 m
Image shape: (720, 1280, 3)
 98%|█████████▊| 1236/1261 [07:07<00:08,  2.93it/s]
1094.78032254 m 370.35563963 m
Image shape: (720, 1280, 3)
 98%|█████████▊| 1237/1261 [07:07<00:08,  2.93it/s]
1194.33757211 m 461.94409571 m
Image shape: (720, 1280, 3)
 98%|█████████▊| 1238/1261 [07:07<00:07,  2.97it/s]
1194.3873525 m 553.119801218 m
Image shape: (720, 1280, 3)
 98%|█████████▊| 1239/1261 [07:08<00:07,  2.98it/s]
945.875180209 m 800.196143197 m
Image shape: (720, 1280, 3)
 98%|█████████▊| 1240/1261 [07:08<00:06,  3.00it/s]
1257.03791728 m 530.48604606 m
Image shape: (720, 1280, 3)
 98%|█████████▊| 1241/1261 [07:08<00:06,  2.99it/s]
1618.70668016 m 446.225256096 m
Image shape: (720, 1280, 3)
 98%|█████████▊| 1242/1261 [07:09<00:06,  3.01it/s]
1601.42858955 m 520.834255465 m
Image shape: (720, 1280, 3)
 99%|█████████▊| 1243/1261 [07:09<00:05,  3.00it/s]
1916.10238206 m 528.13516175 m
Image shape: (720, 1280, 3)
 99%|█████████▊| 1244/1261 [07:09<00:05,  2.99it/s]
1701.61650367 m 651.121870821 m
Image shape: (720, 1280, 3)
 99%|█████████▊| 1245/1261 [07:10<00:05,  3.00it/s]
2081.17904208 m 781.726039413 m
Image shape: (720, 1280, 3)
 99%|█████████▉| 1246/1261 [07:10<00:04,  3.00it/s]
1810.39707716 m 413.937953574 m
Image shape: (720, 1280, 3)
 99%|█████████▉| 1247/1261 [07:10<00:04,  2.99it/s]
1283.41933934 m 584.90450932 m
Image shape: (720, 1280, 3)
 99%|█████████▉| 1248/1261 [07:11<00:04,  3.00it/s]
1700.43112191 m 672.752548633 m
Image shape: (720, 1280, 3)
 99%|█████████▉| 1249/1261 [07:11<00:04,  2.98it/s]
1624.70501489 m 1031.69394454 m
Image shape: (720, 1280, 3)
 99%|█████████▉| 1250/1261 [07:11<00:03,  2.98it/s]
1838.91161277 m 1221.52091425 m
Image shape: (720, 1280, 3)
 99%|█████████▉| 1251/1261 [07:12<00:03,  2.89it/s]
2030.34489731 m 784.310556479 m
Image shape: (720, 1280, 3)
 99%|█████████▉| 1252/1261 [07:12<00:03,  2.88it/s]
1805.31897191 m 792.275715482 m
Image shape: (720, 1280, 3)
 99%|█████████▉| 1253/1261 [07:12<00:02,  2.91it/s]
1859.8094877 m 487.500785034 m
Image shape: (720, 1280, 3)
 99%|█████████▉| 1254/1261 [07:13<00:02,  2.95it/s]
1662.38125504 m 963.676766806 m
Image shape: (720, 1280, 3)
100%|█████████▉| 1255/1261 [07:13<00:02,  2.94it/s]
1492.40542596 m 1197.24923549 m
Image shape: (720, 1280, 3)
100%|█████████▉| 1256/1261 [07:13<00:01,  2.95it/s]
1393.23742719 m 1508.30126813 m
Image shape: (720, 1280, 3)
100%|█████████▉| 1257/1261 [07:14<00:01,  2.94it/s]
1661.06179612 m 562.088437489 m
Image shape: (720, 1280, 3)
100%|█████████▉| 1258/1261 [07:14<00:01,  2.96it/s]
1312.14963012 m 687.319582506 m
Image shape: (720, 1280, 3)
100%|█████████▉| 1259/1261 [07:14<00:00,  2.97it/s]
2696.35721369 m 640.245616092 m
Image shape: (720, 1280, 3)
100%|█████████▉| 1260/1261 [07:15<00:00,  2.99it/s]
3053.16952787 m 880.607965565 m

[MoviePy] Done.
[MoviePy] >>>> Video ready: project_video_out5.mp4 

CPU times: user 7min 11s, sys: 1min 57s, total: 9min 8s
Wall time: 7min 15s
In [415]:
def printImage(img):
    global Count
    #plt.imshow(img)
    print (Count)
    filename = "test/" + str(Count) + '.jpg'
    print (filename)
    img2 = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    cv2.imwrite(filename, img2)
    result = process_image2(img)
    filename = "test/" + str(Count) + 'processed.jpg'
    cv2.imwrite(filename, result)
    Count = Count + 1
    return result
In [416]:
subclip = VideoFileClip("project_video.mp4").subclip(41, 42)
video_out2 = "project_video_out_sub14142.mp4"
Count = 0
In [417]:
video_printed = subclip.fl_image(printImage)
video_printed.write_videofile("test.mp4", audio=False)
0
test/0.jpg
Image shape: (720, 1280, 3)
1105.7093996 m 319.480365708 m
[MoviePy] >>>> Building video test.mp4
[MoviePy] Writing video test.mp4
  0%|          | 0/26 [00:00<?, ?it/s]
1
test/1.jpg
Image shape: (720, 1280, 3)
  4%|▍         | 1/26 [00:00<00:10,  2.37it/s]
1105.7093996 m 319.480365708 m
2
test/2.jpg
Image shape: (720, 1280, 3)
  8%|▊         | 2/26 [00:00<00:09,  2.45it/s]
736.849162863 m 232.659970739 m
3
test/3.jpg
Image shape: (720, 1280, 3)
 12%|█▏        | 3/26 [00:01<00:09,  2.51it/s]
17268.4785119 m 271.103320558 m
4
test/4.jpg
Image shape: (720, 1280, 3)
 15%|█▌        | 4/26 [00:01<00:08,  2.52it/s]
3175.0911821 m 235.962472406 m
5
test/5.jpg
Image shape: (720, 1280, 3)
 19%|█▉        | 5/26 [00:01<00:08,  2.54it/s]
263.321933104 m 306.413442328 m
6
test/6.jpg
Image shape: (720, 1280, 3)
 23%|██▎       | 6/26 [00:02<00:07,  2.55it/s]
1262.65325924 m 353.817512113 m
7
test/7.jpg
Image shape: (720, 1280, 3)
 27%|██▋       | 7/26 [00:02<00:07,  2.55it/s]
266.087395656 m 395.751663391 m
8
test/8.jpg
Image shape: (720, 1280, 3)
 31%|███       | 8/26 [00:03<00:07,  2.55it/s]
152.750640742 m 478.378917718 m
9
test/9.jpg
Image shape: (720, 1280, 3)
 35%|███▍      | 9/26 [00:03<00:06,  2.55it/s]
215.193215776 m 1024.94728913 m
10
test/10.jpg
Image shape: (720, 1280, 3)
 38%|███▊      | 10/26 [00:03<00:06,  2.53it/s]
141.536414034 m 458.987539444 m
11
test/11.jpg
Image shape: (720, 1280, 3)
 42%|████▏     | 11/26 [00:04<00:05,  2.51it/s]
142.052392317 m 296.320037897 m
12
test/12.jpg
Image shape: (720, 1280, 3)
 46%|████▌     | 12/26 [00:04<00:05,  2.51it/s]
143.491042774 m 184.156948389 m
13
test/13.jpg
Image shape: (720, 1280, 3)
 50%|█████     | 13/26 [00:05<00:05,  2.51it/s]
189.405813218 m 167.160102929 m
14
test/14.jpg
Image shape: (720, 1280, 3)
 54%|█████▍    | 14/26 [00:05<00:04,  2.51it/s]
390.477178641 m 138.719548263 m
15
test/15.jpg
Image shape: (720, 1280, 3)
 58%|█████▊    | 15/26 [00:05<00:04,  2.51it/s]
1242.78558003 m 103.32614149 m
16
test/16.jpg
Image shape: (720, 1280, 3)
 62%|██████▏   | 16/26 [00:06<00:03,  2.50it/s]
120.426167013 m 146.486507344 m
17
test/17.jpg
Image shape: (720, 1280, 3)
 65%|██████▌   | 17/26 [00:06<00:03,  2.51it/s]
125.784300621 m 222.225429123 m
18
test/18.jpg
Image shape: (720, 1280, 3)
 69%|██████▉   | 18/26 [00:07<00:03,  2.46it/s]
122.299331503 m 145.580220366 m
19
test/19.jpg
Image shape: (720, 1280, 3)
 73%|███████▎  | 19/26 [00:07<00:02,  2.48it/s]
95.0149537953 m 122.947121674 m
20
test/20.jpg
Image shape: (720, 1280, 3)
 77%|███████▋  | 20/26 [00:07<00:02,  2.50it/s]
225.319942098 m 231.017680605 m
21
test/21.jpg
Image shape: (720, 1280, 3)
 81%|████████  | 21/26 [00:08<00:01,  2.54it/s]
210.102424196 m 491.700064407 m
22
test/22.jpg
Image shape: (720, 1280, 3)
 85%|████████▍ | 22/26 [00:08<00:01,  2.56it/s]
146.465901202 m 343.138235635 m
23
test/23.jpg
Image shape: (720, 1280, 3)
 88%|████████▊ | 23/26 [00:09<00:01,  2.56it/s]
205.966474236 m 355.494339821 m
24
test/24.jpg
Image shape: (720, 1280, 3)
 92%|█████████▏| 24/26 [00:09<00:00,  2.57it/s]
256.372932806 m 283.389039569 m
25
test/25.jpg
Image shape: (720, 1280, 3)
 96%|█████████▌| 25/26 [00:09<00:00,  2.58it/s]
255.906957589 m 332.764530219 m

[MoviePy] Done.
[MoviePy] >>>> Video ready: test.mp4 

In [418]:
video_cap2 = subclip.fl_image(process_image2)
%time video_cap2.write_videofile(video_out2, audio=False)
Image shape: (720, 1280, 3)
1105.7093996 m 319.480365708 m
[MoviePy] >>>> Building video project_video_out_sub14142.mp4
[MoviePy] Writing video project_video_out_sub14142.mp4
  0%|          | 0/26 [00:00<?, ?it/s]
Image shape: (720, 1280, 3)
  4%|▍         | 1/26 [00:00<00:08,  2.95it/s]
1105.7093996 m 319.480365708 m
Image shape: (720, 1280, 3)
  8%|▊         | 2/26 [00:00<00:07,  3.04it/s]
736.849162863 m 232.659970739 m
Image shape: (720, 1280, 3)
 12%|█▏        | 3/26 [00:00<00:07,  3.10it/s]
17268.4785119 m 271.103320558 m
Image shape: (720, 1280, 3)
 15%|█▌        | 4/26 [00:01<00:06,  3.15it/s]
3175.0911821 m 235.962472406 m
Image shape: (720, 1280, 3)
 19%|█▉        | 5/26 [00:01<00:06,  3.18it/s]
263.321933104 m 306.413442328 m
Image shape: (720, 1280, 3)
 23%|██▎       | 6/26 [00:01<00:06,  3.11it/s]
1262.65325924 m 353.817512113 m
Image shape: (720, 1280, 3)
 27%|██▋       | 7/26 [00:02<00:06,  3.15it/s]
266.087395656 m 395.751663391 m
Image shape: (720, 1280, 3)
 31%|███       | 8/26 [00:02<00:05,  3.17it/s]
152.750640742 m 478.378917718 m
Image shape: (720, 1280, 3)
 35%|███▍      | 9/26 [00:02<00:05,  3.16it/s]
215.193215776 m 1024.94728913 m
Image shape: (720, 1280, 3)
 38%|███▊      | 10/26 [00:03<00:05,  3.12it/s]
141.536414034 m 458.987539444 m
Image shape: (720, 1280, 3)
 42%|████▏     | 11/26 [00:03<00:04,  3.08it/s]
142.052392317 m 296.320037897 m
Image shape: (720, 1280, 3)
 46%|████▌     | 12/26 [00:03<00:04,  3.07it/s]
143.491042774 m 184.156948389 m
Image shape: (720, 1280, 3)
 50%|█████     | 13/26 [00:04<00:04,  3.05it/s]
189.405813218 m 167.160102929 m
Image shape: (720, 1280, 3)
 54%|█████▍    | 14/26 [00:04<00:03,  3.05it/s]
390.477178641 m 138.719548263 m
Image shape: (720, 1280, 3)
 58%|█████▊    | 15/26 [00:04<00:03,  3.05it/s]
1242.78558003 m 103.32614149 m
Image shape: (720, 1280, 3)
 62%|██████▏   | 16/26 [00:05<00:03,  3.06it/s]
120.426167013 m 146.486507344 m
Image shape: (720, 1280, 3)
 65%|██████▌   | 17/26 [00:05<00:02,  3.06it/s]
125.784300621 m 222.225429123 m
Image shape: (720, 1280, 3)
 69%|██████▉   | 18/26 [00:05<00:02,  3.06it/s]
122.299331503 m 145.580220366 m
Image shape: (720, 1280, 3)
 73%|███████▎  | 19/26 [00:06<00:02,  3.06it/s]
95.0149537953 m 122.947121674 m
Image shape: (720, 1280, 3)
 77%|███████▋  | 20/26 [00:06<00:01,  3.04it/s]
225.319942098 m 231.017680605 m
Image shape: (720, 1280, 3)
 81%|████████  | 21/26 [00:06<00:01,  3.04it/s]
210.102424196 m 491.700064407 m
Image shape: (720, 1280, 3)
 85%|████████▍ | 22/26 [00:07<00:01,  3.04it/s]
146.465901202 m 343.138235635 m
Image shape: (720, 1280, 3)
 88%|████████▊ | 23/26 [00:07<00:00,  3.04it/s]
205.966474236 m 355.494339821 m
Image shape: (720, 1280, 3)
 92%|█████████▏| 24/26 [00:07<00:00,  3.03it/s]
256.372932806 m 283.389039569 m
Image shape: (720, 1280, 3)
 96%|█████████▌| 25/26 [00:08<00:00,  3.02it/s]
255.906957589 m 332.764530219 m

[MoviePy] Done.
[MoviePy] >>>> Video ready: project_video_out_sub14142.mp4 

CPU times: user 8.4 s, sys: 2.23 s, total: 10.6 s
Wall time: 8.88 s
In [419]:
subclip = VideoFileClip("project_video.mp4").subclip(23, 24)
video_out2 = "project_video_out_sub2324.mp4"
Count = 0
In [420]:
video_cap2 = subclip.fl_image(process_image2)
%time video_cap2.write_videofile(video_out2, audio=False)
Image shape: (720, 1280, 3)
100.302151559 m 203.732459712 m
[MoviePy] >>>> Building video project_video_out_sub2324.mp4
[MoviePy] Writing video project_video_out_sub2324.mp4
  0%|          | 0/26 [00:00<?, ?it/s]
Image shape: (720, 1280, 3)
  4%|▍         | 1/26 [00:00<00:08,  3.05it/s]
100.302151559 m 203.732459712 m
Image shape: (720, 1280, 3)
  8%|▊         | 2/26 [00:00<00:07,  3.11it/s]
1159.17847084 m 105.578569687 m
Image shape: (720, 1280, 3)
 12%|█▏        | 3/26 [00:00<00:07,  3.14it/s]
55.2337657823 m 68.7218422405 m
Image shape: (720, 1280, 3)
 15%|█▌        | 4/26 [00:01<00:06,  3.17it/s]
105.761171769 m 298.049399662 m
Image shape: (720, 1280, 3)
 19%|█▉        | 5/26 [00:01<00:06,  3.18it/s]
65.1255970106 m 182.011646746 m
Image shape: (720, 1280, 3)
 23%|██▎       | 6/26 [00:01<00:06,  3.22it/s]
1635.32525824 m 170.103107729 m
Image shape: (720, 1280, 3)
 27%|██▋       | 7/26 [00:02<00:05,  3.24it/s]
256.170778467 m 336.231984686 m
Image shape: (720, 1280, 3)
 31%|███       | 8/26 [00:02<00:05,  3.26it/s]
335.223822395 m 537.853431682 m
Image shape: (720, 1280, 3)
 35%|███▍      | 9/26 [00:02<00:05,  3.27it/s]
90.2301523231 m 1074.83991378 m
Image shape: (720, 1280, 3)
 38%|███▊      | 10/26 [00:03<00:04,  3.23it/s]
390.40193998 m 231.231782638 m
Image shape: (720, 1280, 3)
 42%|████▏     | 11/26 [00:03<00:04,  3.23it/s]
9293.24525194 m 1065.65259846 m
Image shape: (720, 1280, 3)
 46%|████▌     | 12/26 [00:03<00:04,  3.26it/s]
15.7503565116 m 465.096492943 m
Image shape: (720, 1280, 3)
 50%|█████     | 13/26 [00:04<00:03,  3.27it/s]
10.0877087995 m 319.6128733 m
Image shape: (720, 1280, 3)
 54%|█████▍    | 14/26 [00:04<00:03,  3.30it/s]
47.1080160553 m 247.272109504 m
Image shape: (720, 1280, 3)
 58%|█████▊    | 15/26 [00:04<00:03,  3.28it/s]
30.9384192874 m 354.415983822 m
Image shape: (720, 1280, 3)
 62%|██████▏   | 16/26 [00:04<00:03,  3.26it/s]
1802.02449394 m 512.222165866 m
Image shape: (720, 1280, 3)
 65%|██████▌   | 17/26 [00:05<00:02,  3.29it/s]
33.6773881349 m 5223.7796363 m
Image shape: (720, 1280, 3)
 69%|██████▉   | 18/26 [00:05<00:02,  3.26it/s]
37.0590927207 m 507.66292137 m
Image shape: (720, 1280, 3)
 73%|███████▎  | 19/26 [00:05<00:02,  3.26it/s]
53.886452611 m 222.641092138 m
Image shape: (720, 1280, 3)
 77%|███████▋  | 20/26 [00:06<00:01,  3.25it/s]
103.163061424 m 989.740449879 m
Image shape: (720, 1280, 3)
 81%|████████  | 21/26 [00:06<00:01,  3.27it/s]
39.6824198688 m 652.480060005 m
Image shape: (720, 1280, 3)
 85%|████████▍ | 22/26 [00:06<00:01,  3.24it/s]
1430.04747665 m 151.336639119 m
Image shape: (720, 1280, 3)
 88%|████████▊ | 23/26 [00:07<00:00,  3.25it/s]
106.68786855 m 98.9218169385 m
Image shape: (720, 1280, 3)
 92%|█████████▏| 24/26 [00:07<00:00,  3.27it/s]
57.7778116574 m 351.023098917 m
Image shape: (720, 1280, 3)
 96%|█████████▌| 25/26 [00:07<00:00,  3.29it/s]
90.9144238051 m 90.6249236688 m

[MoviePy] Done.
[MoviePy] >>>> Video ready: project_video_out_sub2324.mp4 

CPU times: user 8.16 s, sys: 1.82 s, total: 9.97 s
Wall time: 8.35 s
In [ ]:
 
In [269]:
video_printed = subclip.fl_image(printImage)
video_printed.write_videofile("test.mp4", audio=False)
0
test/0.jpg
Image shape: (720, 1280, 3)
269.172199934 m 232.813917888 m
[MoviePy] >>>> Building video test.mp4
[MoviePy] Writing video test.mp4
  0%|          | 0/26 [00:00<?, ?it/s]
1
test/1.jpg
Image shape: (720, 1280, 3)
  4%|▍         | 1/26 [00:00<00:10,  2.46it/s]
269.172199934 m 232.813917888 m
2
test/2.jpg
Image shape: (720, 1280, 3)
  8%|▊         | 2/26 [00:00<00:09,  2.52it/s]
11363.1571834 m 123.177511778 m
3
test/3.jpg
Image shape: (720, 1280, 3)
 12%|█▏        | 3/26 [00:01<00:08,  2.56it/s]
885.316311984 m 80.9329243085 m
4
test/4.jpg
Image shape: (720, 1280, 3)
 15%|█▌        | 4/26 [00:01<00:08,  2.59it/s]
689.516875023 m 304.810295831 m
5
test/5.jpg
Image shape: (720, 1280, 3)
 19%|█▉        | 5/26 [00:01<00:08,  2.60it/s]
303.212674934 m 588.269406057 m
6
test/6.jpg
Image shape: (720, 1280, 3)
 23%|██▎       | 6/26 [00:02<00:07,  2.56it/s]
1716.43304101 m 357.051565526 m
7
test/7.jpg
Image shape: (720, 1280, 3)
 27%|██▋       | 7/26 [00:02<00:07,  2.53it/s]
944.285123284 m 501.037397635 m
8
test/8.jpg
Image shape: (720, 1280, 3)
 31%|███       | 8/26 [00:03<00:07,  2.52it/s]
288.685785215 m 578.003530969 m
9
test/9.jpg
Image shape: (720, 1280, 3)
 35%|███▍      | 9/26 [00:03<00:06,  2.49it/s]
372.534585281 m 958.677246227 m
10
test/10.jpg
Image shape: (720, 1280, 3)
 38%|███▊      | 10/26 [00:03<00:06,  2.52it/s]
277.0778086 m 303.817360289 m
11
test/11.jpg
Image shape: (720, 1280, 3)
 42%|████▏     | 11/26 [00:04<00:05,  2.58it/s]
261.835933258 m 1147.967265 m
12
test/12.jpg
Image shape: (720, 1280, 3)
 46%|████▌     | 12/26 [00:04<00:05,  2.58it/s]
2173.27870768 m 488.633488643 m
13
test/13.jpg
Image shape: (720, 1280, 3)
 50%|█████     | 13/26 [00:05<00:04,  2.60it/s]
200.7776025 m 348.667888953 m
14
test/14.jpg
Image shape: (720, 1280, 3)
 54%|█████▍    | 14/26 [00:05<00:04,  2.63it/s]
113.670463185 m 247.974854888 m
15
test/15.jpg
Image shape: (720, 1280, 3)
 58%|█████▊    | 15/26 [00:05<00:04,  2.61it/s]
246.420385664 m 353.785591164 m
16
test/16.jpg
Image shape: (720, 1280, 3)
 62%|██████▏   | 16/26 [00:06<00:03,  2.62it/s]
663.193532263 m 580.766048399 m
17
test/17.jpg
Image shape: (720, 1280, 3)
 65%|██████▌   | 17/26 [00:06<00:03,  2.62it/s]
1434.37492174 m 3520.63179321 m
18
test/18.jpg
Image shape: (720, 1280, 3)
 69%|██████▉   | 18/26 [00:06<00:03,  2.60it/s]
2013.6724084 m 511.162040794 m
19
test/19.jpg
Image shape: (720, 1280, 3)
 73%|███████▎  | 19/26 [00:07<00:02,  2.56it/s]
251.600836483 m 232.647170383 m
20
test/20.jpg
Image shape: (720, 1280, 3)
 77%|███████▋  | 20/26 [00:07<00:02,  2.55it/s]
1308.10186765 m 885.660450373 m
21
test/21.jpg
Image shape: (720, 1280, 3)
 81%|████████  | 21/26 [00:08<00:01,  2.57it/s]
433.651542702 m 594.321995485 m
22
test/22.jpg
Image shape: (720, 1280, 3)
 85%|████████▍ | 22/26 [00:08<00:01,  2.60it/s]
336.515109025 m 155.770234143 m
23
test/23.jpg
Image shape: (720, 1280, 3)
 88%|████████▊ | 23/26 [00:08<00:01,  2.58it/s]
346.801246921 m 98.2008427671 m
24
test/24.jpg
Image shape: (720, 1280, 3)
 92%|█████████▏| 24/26 [00:09<00:00,  2.60it/s]
297.692782126 m 349.83936623 m
25
test/25.jpg
Image shape: (720, 1280, 3)
 96%|█████████▌| 25/26 [00:09<00:00,  2.63it/s]
184.774747054 m 90.6249236688 m

[MoviePy] Done.
[MoviePy] >>>> Video ready: test.mp4 

In [ ]: